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Google SRE NYC proudly announces our last Google SRE NYC Tech Talk for 2025.

This event is co-sponsored by sentry.io. Thank you Sentry for your partnership!

Let's farewell 2025 with three amazing interactive short talks on Site Reliability and DevOps topics! As always the event will include an opportunity to mingle with the speakers and attendees over some light snacks and beverages after the talks.

The Meetup will take place on Tuesday, 16th of December 2025 at 6:00 PM at our Chelsea Markets office in NYC. The doors will open at 5:30 pm. Pls RSVP only if you're able to attend in-person, there will be no live streaming.

When RSVP'ing to this event, please enter your full name exactly as it appears on your government issued ID. You will be required to present your ID at check in.

Agenda: Paul Jaffre - Senior Developer Experience Engineer\, sentry.io One Trace to Rule Them All: Unifying Sentry Errors with OpenTelemetry tracing SREs face the challenge of operating reliable observability infrastructure while avoiding vendor lock-in from proprietary APM (Application Performance Monitoring) solutions. OpenTelemetry has become the standard for instrumenting applications, allowing teams to collect traces, metrics, and logs. But raw telemetry data isn't enough. SREs need tools to visualize, debug, and respond to production incidents quickly. Sentry now supports OTLP, enabling teams to send OpenTelemetry data directly to Sentry for analysis. This talk covers how Sentry's OTLP support works in practice: connecting frontend and backend traces across services, correlating logs with distributed traces, and using tools to identify slow queries and performance bottlenecks. We'll discuss the practical benefits for SREs, like faster incident resolution, better cross-team debugging, and the flexibility to change observability backends without re-instrumenting code. Paul’s background spans engineering, product management, UX design, and open source. He has a soft spot for dev tools and loses sleep over making things easy to understand and use. Paul has a dynamic professional background, from strategy to stability. His time at Krossover Intelligence established a strong foundation by blending Product Management with hands-on development, and he later focused on core reliability at MakerBot, where he implemented automated end-to-end testing and drove performance improvements. He then extended this expertise in stability and scale at Cypress.io, where he served as a Developer Experience Engineer, focusing on improving workflow, contribution, and usability for their widely adopted open-source community.

Thiara Ortiz - Cloud Gaming SRE Manager\, Netflix Managing Black Box Systems SREs often face ambiguity when managing black box systems (LLMs, Games, Poorly Understood Dependencies). We will discuss how Netflix monitors service health as black boxes using multiple measurement techniques to understand system behavior, aligning with the need for robust observability tools. These strategies are crucial for system reliability and user experience. By proactively identifying and resolving issues, we ensure smoother playback experience and maintain user trust, even as the platform continues to evolve and gain maturity. The principles shared within this talk can be expanded to other applications such as AI reliability in data quality and model deployments.

Thiara has worked at some of the largest internet companies in the world, Meta and Netflix. During her time at Meta, Thiara found a passion for distributed systems and bringing new hardware into production. Always curious to explore new solutions to complex problems, Thiara developed Fleet Scanner, internally known as Lemonaid, to perform memory, compute, and storage benchmarks on each Meta server in production. This service runs on over 5 million servers and continues to be utilized at Meta. Since Meta, Thiara has been working at Netflix as a Senior CDN Reliability engineer, and now, Cloud Gaming SRE Manager. When incidents occur and Netflix's systems do not behave as expected, Thiara can be found working and engaging the necessary teams to remediate these issues.

Andrew Espira - Platform and Site Reliability Engineer\, Founding Engineer kustode ML-Powered Predictive SRE: Using Behavioral Signals to Prevent Cluster Inefficiencies Before They Impact Production SREs managing ML clusters often discover resource inefficiencies and queue bottlenecks only after they've impacted production services. This talk presents a machine learning approach to predict these issues before they occur, transforming SRE from reactive firefighting to proactive system optimization. We demonstrate how to build predictive models using production cluster traces that identify two critical failure modes: (1) GPU under-utilization relative to requested resources, and (2) abnormal queue wait times that indicate impending service degradation. The SRE practitioners will learn how to extract early warning indicators from standard cluster logs, build ML models that provide actionable confidence scores for operational decisions, and take practical steps to integrate predictive analytics into existing SRE toolchains to achieve 50%+ reduction in resource waste and queue-related incidents This talk bridges the gap between traditional SRE observability and modern predictive analytics, showing how teams can evolve from reactive monitoring to intelligent, forward-looking reliability engineering" Andrew has over 8 years of experience architecting and maintaining large-scale distributed systems. He is the Founding Engineer of Kustode (kustode.com), where he develops cutting-edge reliability and observability solutions for modern infrastructure in the Insurance and health care solutions space. Currently pursuing graduate studies in Data Science at Saint Peter's University, he specializes in the intersection of reliability engineering and artificial intelligence. His research focuses on applying machine learning to operational challenges, with publications in peer-reviewed venues including ScienceDirect. He's passionate about making complex systems more predictable and maintainable through data-driven approaches. When not optimizing cluster performance or building the next generation of observability tools, Andrew enjoys contributing to open-source projects and mentoring early-career engineers in the SRE community.

Our Tech Talks series are for professional development and networking: no recruiters, sales or press please! Google is committed to providing a harassment-free and inclusive conference experience for everyone, and all participants must follow our Event Community Guidelines. The event will be photographed and video recorded.

Event space is limited! A reservation is required to attend. Reserve your spot today and share the event details with your SRE/DevOps friends 🙂

Zoom link: https://us02web.zoom.us/j/82308186562

Talk #0: Introductions and Meetup Updates by Chris Fregly and Antje Barth

Talk #1: Advanced and Accelerated Data Curation and Visualizations for LLMs with NVIDIA CuML, DBSCAN, and tSNE (Performance-focused) by Theo Viel and Dante Gama Dessavre @ NVIDIA

This technical talk will focus on applying high-performance techniques to data curation and visualization tasks for LLMs with NVIDIA cuML by AI performance specialist Theo Viel. It will also include a performance deep dive on CUDA-X library cuML and why GPUs excel at boosting performance for DBSCAN and tSNE algorithms with ML engineering and AI Infrastructure manager Dante Gama Dessavre.

Talk #2: Neurips 2025 Summary by Chris Fregly High-level summary of hot AI-performance-related topics at Neurips 2025!

Zoom link: https://us02web.zoom.us/j/82308186562

Related Links Github Repo: http://github.com/cfregly/ai-performance-engineering/ O'Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/ YouTube: https://www.youtube.com/@AIPerformanceEngineering Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025 9 AM Pacific Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.

** Important RSVP HERE (Due to room capacity and venue security, it is required to pre-register at the link for admission)

Welcome to the AI meetup in London. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Tech Talk: What you need to know about AI factories Speaker: Matt Shore (High Performance Computing & Artificial Intelligence) Abstract: As organisations start to move from proof of concept to production, they need to consider how to build their infrastructure from the ground up to be completely optimised for the next wave of AI’s requirements. From data to power and space, HPE will provide a whistlestop tour of the latest thinking across the AI stack, as well as what it means for each type of role in the organisation.

Tech Talk: Accessing and building with open-source models Speaker: Darin Verheijke (Recursal ai) Abstract: In this session, I will discuss the recent improvements of open-source LLM models, the difficulties in running these open-source models for yourself/your company, how you can easily access and make use of all these open-source models through Hugging Face and Featherless.ai and demo some self-made open-source applications for every developer to use.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in London and 500K+ worldwide.

Register for the event to reserve your spot!

Date and Time Feb 7, 2025 from 5:30 PM to 8:30 PM

Location The Meetup will take place at MotionLab.Berlin, Bouchéstraße 12/Halle 20 in Berlin

Smart Data Loops: A New Paradigm for AI Development and Anomaly Detection

In the era of autonomous driving, the quality and efficiency of AI development hinge on the ability to manage data intelligently. This talk introduces the concept of Smart Data Loop, a novel paradigm that revolutionizes data handling by improving out-of-distribution detection, and leveraging trigger functions to refine AI models continuously. We will explore how these innovative approaches enhance anomaly detection and streamline AI workflows.

About the Speaker

Dr. Azarm Nowzad holds a PhD in Computer Science and serves as the Technical Project Lead and Product Owner for “Data for AI” at Continental Automotive. She is currently leading the publicly funded project “justbetterDATA”, which focuses on developing efficient and highly accurate data generation methods for AI applications, particularly in the field of autonomous driving. With her expertise in computer vision and AI, she plays a pivotal role in advancing data-driven solutions for next-generation mobility.

All About Agentic AI

Today, the concept of Agentic AI is shaping how we think about intelligent systems. These are AI systems designed to act autonomously, making decisions, completing tasks, and interacting with their environment—beyond traditional AI models. Understanding how to design and develop Agentic AI products is essential for staying ahead in the competitive landscape of AI-driven innovation. In this talk, Dr. Arman Nassirtoussi introduces Agentic AI. He’ll cover how these systems differ from standard AI, the evolving architectures that support them, and why they’re becoming critical.

About the Speaker

Dr. Arman Nassirtoussi earned his PhD in AI over a decade ago, focusing on predictive AI algorithms for intraday financial trading using Natural Language Processing (NLP), sentiment analysis, and text mining of online news. His main publication has quickly received over 1,200 citations on Google Scholar. Arman has led large data engineering, data science, and AI teams at companies like Henkel, Zalando, and T-Systems, helping build infrastructure, platforms, and products with a major focus on personalization and product analytics in e-commerce. Arman has also created a number of startups in multiple countries, and he is currently shaping a new one in the Agentic AI space.

Bridging Minds and Machines: Aligning Human Behavior and Machine Algorithm

As AI systems increasingly support human decision-making, integrating human-centered design principles into ML engineering has become essential. This talk bridges the foundational concepts of Human-Computer Interaction (HCI) with the complex demands of algorithmic decision-making, focusing on bidirectional Human-AI alignment, trust calibration, and Reciprocal Human-Machine Learning (RHML).

We explore the necessity of embedding human behavior and neurocognitive feedback loops into ML pipelines to enable adaptive and trustworthy systems. Addressing overtrust, undertrust, and trust miscalibration, we emphasize aligning ML systems with both high-performance metrics and user behavior, ensuring systems are effective and ethically aligned.

About the Speaker

Anke Borchers is an AI Strategist and Consultant specializing in Machine Learning (ML), Generative AI, and Trustworthy AI. With a background in Industrial and Communication Design and over 15 years of experience in innovation and business strategy, she bridges the gap between human-centered design and advanced AI systems.

Dedicated to crafting tailored solutions for the medical and business sectors, Anke highlights the critical importance of human-centered AI systems. She offers deep expertise in cognitive and machine decision-making, as well as AI Alignment, empowering organizations to develop AI solutions that are high-performing, ethically sound, and optimized to address user needs effectively.

Attention is All We Need: Using Transformers in Vision Tasks

Attention mechanism, initially developed for natural language processing, is now being effectively applied in Computer Vision. This talk will focus on how attention enables Visual Transformers to capture context and why they are overpowering the classical approaches to vision tasks.

About the Speaker

Kira Kravets is a Machine Learning engineer at Kertos, specializing in LLMs and the development of trustworthy AI systems. With experience in Computer Vision, particularly in the highly demanding medical field, she is passionate about building real-world AI applications with all the limitations and restrictions of production environments.

** Important register on the event website (Due to limited room capacity, you must pre-register at the link for admission).

Welcome to the AI meetup in London, in collaboration with Nexgen Cloud. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Tech Talk: From Nodes to Knowledge: GraphRAG for Everyone Speaker: Sethu Pavan (Microsoft) Abstract: In this talk, we’ll explore how GraphRAG works, why it’s better than regular RAG, limitations, considerations for GraphRAG and a demo.

Tech Talk: Prophetic Proteins - Unravelling Disease Fate Through ML Speaker: Dr Harry Whitwell (Imperial College) Abstract: Predicting individual cancer risk is challenging due to complex biology and varied patient responses. Traditional biomarker tests often fall short. We used machine learning, developing "synolitic networks" to improve early cancer detection and predict outcomes during COVID. Our current research focuses on enhancing personalized medicine and biological understanding through these networks.

Tech Talk: GPUs at Scale - Trials of a GPUaaS Provider Speaker: Mischa van Kesteren (Nexgen Cloud) Abstract: In the rapidly evolving landscape of machine learning, managing large-scale GPU infrastructure has become a critical challenge for AI practitioners. This presentation delves into the trenches of GPU operations. Through these real-world lessons, we explore how GPU-as-a-Service (GPUaaS) solutions address these pain points, offering scalability, flexibility, and cutting-edge hardware access. Join us for a deep dive into the challenges of computation at scale, the lessons learned from hands-on experience, and a discussion of emerging strategies in GPU infrastructure management.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 10,000+ AI developers in London or 400K+ worldwide.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations *

** RSVP here (Due to room capacity and venue security, it is required to pre-register at the link for admission)

Welcome to the monthly AI meetup in Berlin, in collaboration with GetYourGuide. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

​Curious about the practical implications of AI in the tech industry? Join us to learn how companies across the globe use AI in Production. In this session, we're focusing on the hands-on applications of AI in the industry.

This meetup is relevant to ML and AI practitioners, but we invite anyone passionate about AI and ML to join us at our office in Prenzlauer Berg. You can expect great talks, pizza, drinks, and networking with people building with AI in Berlin.

Agenda: * 6:00pm\~6:50pm: Checkin, Food/drink and Networking * 7:00pm\~8:30pm: Tech talks and Q&A * 8:30pm: Open discussion and Mixer

Tech Talk: Enhancing Personalization with Real-Time ML Speaker: ​​Mihail Douhaniaris (GetYourGuide) Abstract: ​Discover how GetYourGuide personalizes user experiences using real-time machine learning. Learn how we evolved our ranking infrastructure from full batch pipelines to real-time feature generation and serving, ensuring personalized and relevant activity recommendations for millions of users daily. Gain insights into the critical role of feature stores in maintaining accurate data and the importance of model observability in detecting and addressing data drift.​

Tech Talk: Graph RAG Speaker: ​​Jakob Pörschmann (Google Cloud ) Abstract: ​​In this talk, we will find out whether text embedding RAG is actually dead. We will explore Graph RAG's advantages and disadvantages. Let’s also discuss implementation strategies and compare costs and build your understanding so you know where to get started.​

Tech Talk: Introduction to Causal AI Speaker: ​​Luyolo Magangane (HelloFresh) Abstract: ​We’ll walk through applying the framework in enabling customers to safely tweak business objective parameters when using machine learning applications.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

​​​Important note: Please be advised that this event will be recorded and photographed, and we will have a photographer on-site. If you prefer not to be included in any recordings or photographs, please do not hesitate to let us know during the event. Your comfort and privacy are important to us.

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only have the chance to speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 8,000+ AI developers in Berlin or 350K+ worldwide.

AICamp Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations

🎙️ Featured Talks:

Everything I wish I knew before I started building an LLM product Ed Shee, AI Engineer and Founder at Ignitus

Introduction to Foundation Models: A Whirlwind Tour of Modern AI Tools and Techniques Ramon Perez, Developer Relations at Seldon

Futures Forum is a monthly meetup powered by SurrealDB that explores the AI revolution, with a particular focus on LLMs (Large Language Models). With actionable insights, the series will include introductory and advanced topic discussions by a dynamic and diverse range of expert speakers, live demos, news updates and panel discussions. Plus plenty of time for networking and refreshments.

💻 The event will be available to watch afterwards via the SurrealDB Futures Forum playlist here: http://bit.ly/3vHHyVR.

📣 Talks and demos by experts in their fields 🗣️ Friendly networking 🍕 Delicious bites and 🍦ice cream 🍹 Tasty drinks – including boozy and alcohol-free options, sponsored by Something & Nothing 😆 Informative, inclusive and fun!

Agenda

18:30 - 19:00 Welcome Drinks Attendees arrive – grab a drink, explore the space and mingle.

19:00 - 19:30 What Nobody Tells You About Building LLM Products Ed Shee, AI Engineer and Founder at Ignitus

Curious about building awesome apps with Large Language Models (LLMs)? Come join Ed as he shares everything he wishes he knew before he got started. This talk is packed with real-world insights, including:

• Clever techniques for Retrieval-Augmented Generation (RAG)
• Crafting prompts that actually work
• Keeping LLMs on track and under control
• Ensuring your text outputs are top-notch
• Building smart and efficient agents

Plus, Ed will give you a look into the future of LLM apps and what to expect next. Whether you're new to the game or a seasoned developer, you'll leave with practical tips and fresh ideas to power up your projects.

19:30 - 20:00 Social & Tasty Bites A fun and informal way to connect with others in the tech community. Grab a slice of pizza or summer roll, hop on the Oculus or chat with other attendees and the SurrealDB team.

20:00 - 20:30 Introduction to Foundation Models: A Whirlwind Tour of Modern AI Tools and Techniques Ramon Perez, Developer Relations at Seldon

In this talk, we'll dive deep into what foundation models are, why they matter, and how to get started with them. We'll start with an introduction, breaking down the basics, before exploring their wide-ranging applications. From there, we'll go through the process of creating a tiny foundation model and how to use it effectively with prompts. We'll also address common challenges in working with these models and discuss strategies for deployment and post-deployment maintenance. This will be a comprehensive tour through the lifecycle of foundation models, and no prior experience in AI is required. If you’re looking to build some foundational knowledge about machine learning and these new technologies, this talk is for you.

20:30 - 20:45 Panel Discussion & Q+A Lizzie Holmes, Ramon Perez, Ed Shee

20:45 - 21:15 Social & Sweet Treats

FAQs

Who’s this event for? For anyone wanting to learn more about the way that AI and LLMs are shaping the world. What’s an LLM? A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand, summarise, generate and predict new content. LLMs power chat apps like ChatGPT, but also they are increasingly underlying almost every aspect of AI. Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where SurrealDB Social is held. What's a SurrealDB event like? Check out photos from our previous events at https://surrealdb.gallery. Who are SurrealDB? SurrealDB is a modern cloud-native multi-model database that allows users and developers to focus on building their applications rather than architecting and managing their infrastructure with features like SurrealQL, Live Queries, Search, Change Data Capture, and ML. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information.

📣 This event has now SOLD OUT! As our venue has limited capacity, we will be closing the event to attendees once capacity has been reached, so please arrive as close to to 18:30 as possible to guarantee your place.

🎙️ Featured Talks:

Building better LLM products with Evaluations and Analytics Henry Scott-Green

LLMs vs. Real-World Questions: Can RAG Bridge the Gap? Mohammed Talib

Futures Forum is a monthly meetup powered by SurrealDB that explores the AI revolution, with a particular focus on LLMs (Large Language Models). With actionable insights, the series will include introductory and advanced topic discussions by a dynamic and diverse range of expert speakers, live demos, news updates and panel discussions. Plus plenty of time for networking and refreshments.

💻 The event will be available to watch afterwards via the SurrealDB Futures Forum playlist here: http://bit.ly/3vHHyVR.

📣 Talks and demos by experts in their fields 🗣️ Friendly networking 🍕 Delicious bites and 🍦ice cream 🍹 Tasty drinks – including boozy and alcohol-free options, sponsored by Something & Nothing 😆 Informative, inclusive and fun!

Agenda

18:30 - 19:00 Welcome Drinks Attendees arrive – grab a drink, explore the space and mingle.

19:00 - 19:30 Building better LLM products with Evaluations and Analytics Henry Scott-Green So you’ve built your first LLM product. Now what? You need to understand how people are using it, how its performing, and how you can make it better. That’s where LLM evals and analytics come in. But these are totally different from deterministic applications - you need to understand mountains of text. This talk will cover the key considerations for building great end user experiences with LLMs.

19:30 - 20:00 Social & Tasty Bites A fun and informal way to connect with others in the tech community. Grab a slice of pizza or summer roll, hop on the Oculus or chat with other attendees and the SurrealDB team.

20:00 - 20:30 LLMs vs. Real-World Questions: Can RAG Bridge the Gap? Mohammed Talib Large Language Models (LLMs) have revolutionised natural language processing, demonstrating remarkable capabilities in text generation, translation, and more. However, when faced with questions requiring specific knowledge or complex reasoning, LLMs can fall short. Retrieval-Augmented Generation (RAG) offers a solution, integrating LLMs with external knowledge sources to deliver more factually grounded and informative responses.

20:30 - 21:00 Social & Sweet Treats

FAQs

Who’s this event for? For anyone wanting to learn more about the way that AI and LLMs are shaping the world. What’s an LLM? A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand, summarise, generate and predict new content. LLMs power chat apps like ChatGPT, but also they are increasingly underlying almost every aspect of AI. Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where SurrealDB Social is held. What's a SurrealDB event like? Check out photos from our previous events at https://surrealdb.gallery. Who are SurrealDB? SurrealDB is a modern cloud-native multi-model database that allows users and developers to focus on building their applications rather than architecting and managing their infrastructure with features like SurrealQL, Live Queries, Search, Change Data Capture, and ML. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information.

*** RSVP: https://www.aicamp.ai/event/eventdetails/W2024032710 (Due to limited room capacity, you must pre-register at the link for admission).

Welcome to the AI meetup in London. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Agenda: * 6:00pm\~7:00pm: Checkin, Food/drink and Networking * 7:00pm\~9:00pm: Tech talks and Q&A * 9:00pm: Open discussion and Mixer

Tech Talk: Building GenAI and ML systems with OSS Metaflow Speaker: Hugo Bowne-Anderson (Outerbounds) Abstract: This talk explores a framework for how data scientists can deliver value with Generative AI: How can you embed LLMs and foundation models into your pre-existing software stack? How can you do so using Open Source Python? What changes about the production machine learning stack and what remains the same? This talk is aimed squarely at (data) scientists and ML engineers who want to focus on the science, data, and modeling, but want to be able to access all their infrastructural, platform, and software needs with ease!

Tech Talk: Harmony, Open source AI tool for psychology research Speaker: Thomas Wood (Fast Data Science) Abstract: In this talk, I will discuss AI for social sciences research and how to build a research tool with NLP and AI with open source tool Harmony, funded by Wellcome.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 10,000+ AI developers in London or 300K+ worldwide.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page) *

🎙️ Featured Talks: Improve your Apps with Generative AI Christoffer Noring

Panel Discussion: Opportunities presented by AI: what are the opportunities presented by AI both for consumers and companies? What is necessary to be able to seize those opportunities?

--

Futures Forum is a new monthly meetup, powered by SurrealDB, that explores the AI revolution, with a particular focus on LLMs (Large Language Models). With actionable insights, the series will include introductory and advanced topic discussions by a dynamic and diverse range of expert speakers, live demos, news updates and panel discussions. Plus plenty of time for networking and refreshments.

📣 Talks and demos by experts in their fields 🗣️ Friendly networking 🍕 Delicious bites – Homeslice, 🥗 Kaleido Rolls and 🍦ice cream 🍹 Tasty drinks – including boozy and alcohol-free options, sponsored by Something & Nothing 😆 Informative, inclusive and fun!

Agenda

18:30 - 19:00 Welcome Drinks Attendees arrive – grab a drink, explore the space and mingle.

19:00 - 19:30 Featured Talk Christoffer Noring: Improve your Apps with Generative AI Maybe you've used ChatGPT, or maybe everyone is talking about AI but you want to know how you can add that for your apps to benefit customers. In this talk you'll learn how to:

• Get the most out of your prompting through tips and tricks and patterns
• Integrate AI in your apps
• Leverage different types of AI from text , images to search

19:30 - 20:00 Social & Tasty Bites A fun and informal way to connect with others in the tech community. Grab a slice of pizza or summer roll, hop on the Oculus or chat with other attendees and the SurrealDB team.

20:00 - 20:45 Panel Discussion with Audience Q&As Opportunities presented by AI: what are the opportunities presented by AI both for consumers and companies? What is necessary to be able to seize those opportunities?

20:45 - 21:15 Social & Sweet Treats

FAQs

Who’s this event for? For anyone wanting to learn more about the way that AI and LLMs are shaping the world. What’s an LLM? A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand, summarise, generate and predict new content. LLMs power chat apps like ChatGPT, but also they are increasingly underlying almost every aspect of AI. Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where SurrealDB Social is held. What's a SurrealDB event like? Check out photos from our previous events at https://surrealdb.gallery. Who are SurrealDB? SurrealDB is a modern cloud-native multi-model database that allows users and developers to focus on building their applications rather than architecting and managing their infrastructure with features like SurrealQL, Live Queries, Search, Change Data Capture, and ML. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information.

🎙️ Featured Talks: Creating the perfect new hire: A Custom GPT for your business Minisha Goel Tools of Tomorrow: The AI Engineer's Guide to Accelerated Innovation Sam Shapley

Futures Forum is a new monthly meetup, powered by SurrealDB, that explores the AI revolution, with a particular focus on LLMs (Large Language Models). With actionable insights, the series will include introductory and advanced topic discussions by a dynamic and diverse range of expert speakers, live demos, news updates and panel discussions. Plus plenty of time for networking and refreshments.

📣 Talks and demos by experts in their fields 🗣️ Friendly networking 🍕 Delicious bites – Homeslice, 🥗 Kaleido Rolls and 🍦ice cream 🍹 Tasty drinks – including boozy and alcohol-free options, sponsored by Something & Nothing 😆 Informative, inclusive and fun! 🇬🇧 + 💻 Join us live in central London (Oxford Circus) for the in-person event, the talks will also be live streamed and available to watch via Vimeo.

Agenda

18:30 - 19:05 Welcome Drinks Attendees arrive – grab a drink, explore the space and mingle.

19:00 - 19:30 Creating the perfect new hire: A Custom GPT for your business Minisha Goel You've probably heard of ChatGPT ? But have you wondered how you can take ChatGPT's power and tailor it specifically to meet your unique business? Well that's where Custom GPTs come in. In this talk we cover top use cases for ChatGPT within business and how to create your own Custom GPTs to serve your team's capabilities. Whether you're looking to get some inspiration, or learn new ways to configure Custom GPT's, this talk will equip you with the tips and tricks to make it happen.

19:30 - 20:00 Social & Tasty Bites A fun and informal way to connect with others in the tech community. Grab a slice of pizza or summer roll, hop on the Oculus or chat with other attendees and the SurrealDB team.

20:00 - 20:20 Tools of Tomorrow: The AI Engineer's Guide to Accelerated Innovation Sam Shapley In this talk, we'll go on a journey through the accelerating world of AI development, showcasing how AI dramatically speeds up system creation and project execution. We'll delve into the nuances of orchestrating AI workflows and intelligent systems using prompting techniques and cutting-edge AI engineering across frontier multimodal models. The highlight will be building an audience-sourced app idea in real-time using OpenAI Launchpad, Sam's own system for creating instant apps.

20:20 - 20:45 Panel Discussion with Audience Q&As

20:50 - 21:15 Social & Sweet Treats

FAQs

Who’s this event for? For anyone wanting to learn more about the way that AI and LLMs are shaping the world. What’s an LLM? A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand, summarise, generate and predict new content. LLMs power chat apps like ChatGPT, but also they are increasingly underlying almost every aspect of AI. Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where SurrealDB Social is held. What's a SurrealDB event like? Check out photos from our previous events at https://surrealdb.gallery. Who are SurrealDB? SurrealDB is a modern cloud-native multi-model database that allows users and developers to focus on building their applications rather than architecting and managing their infrastructure with features like SurrealQL, Live Queries, Search, Change Data Capture, and ML. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information.

** RSVP: https://www.aicamp.ai/event/eventdetails/W2024020109 *******

Description: Welcome to the monthly in-person AI meetup in Paris. Join us for deep dive tech talks on AI/ML, food/drink, networking with speakers and fellow developers.

Tech Talk: Cloud infrastructure for LLM Speaker: Fabien Da Silva @Scaleway Abstract: In this talk, I will detail what kind of infrastructures are necessary to support Large Language Model training and inference, and how they are built.

Tech Talk: How to deploy a LLM in an Enterprise environment Speaker: Loic Boutet @Safebrain **Abstract:**Enterprise customer IT environment can have specificities that makes it challenging to deploy Large Language Models as we know them. In this talk, I will detail his journey in implementing such models and the best practices he learned.

Tech Talk: Symbolic AI in a Generative AI world Speaker: Killian Vermersch @Golem.ai **Abstract:**Symbolic AI are human readable non ML methods to solve problems dating back from the 50s. How is it possible to use them in our modern AI paradigm and what are their benefits ?

Tech Talk: How to explain LLMs ? First evaluate Speaker: Bastien Zimmermann @Craft AI **Abstract:**Large Language Model explainability is a hot topic in ML research. I will detail why LLM evaluation methods allow us to better understand their internal behavior, and how they work.

Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 10,000+ local or 300K+ developers worldwide.

Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations Join Slack (search and join the #paris channel) \| Join Discord

💻 Livestreams: 19:00 - Generative AI & LLMs – An Introduction 20:00 - Build Your Own Chatbot - With No Code + Panel Discussion

Futures Forum is a new monthly meetup, powered by SurrealDB, that explores the AI revolution, with a particular focus on LLMs (Large Language Models). With actionable insights, the series will include introductory and advanced topic discussions by a dynamic and diverse range of expert speakers, live demos, news updates and panel discussions. Plus plenty of time for networking and refreshments.

📣 Talks and demos by experts in their fields 🗣️ Friendly networking 🍕 Delicious bites – Homeslice, 🥗 Kaleido Rolls and 🍦ice cream 🍹 Tasty drinks – including boozy and alcohol-free options, sponsored by Something & Nothing 😆 Informative, inclusive and fun! 🇬🇧 + 💻 Join us live in central London (Oxford Circus) for the in-person event, the talks will also be live streamed and available to watch via Vimeo.

Agenda

18:30 - 19:05 Welcome Drinks Attendees arrive – grab a drink, explore the space and mingle.

19:00 - 19:30 Generative AI & LLMs – An Introduction Julian Darley, Raphael Darley Resident Futures Forum speaker Julian Darley gives a brief introduction to generative AI, AI agents and highlights some of the latest LLM industry news.

19:30 - 20:00 Social & Tasty Bites A fun and informal way to connect with others in the tech community. Grab a slice of pizza or summer roll, hop on the Oculus or chat with other attendees and the SurrealDB team.

20:00 - 20:20 Build Your Own Chatbot - With No Code Noah Santoni Noah from WhatAIdea will teach you how to build a chatbot using Flowise in less than five minutes!

20:20 - 20:45 Panel Discussion with Audience Q&As Julian Darley, Raphael Darley, Tobie Morgan Hitchcock, Noah Santoni

20:50 - 21:15 Social & Sweet Treats

FAQs

Who’s this event for? For anyone wanting to learn more about the way that AI and LLMs are shaping the world. What’s an LLM? A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand, summarise, generate and predict new content. LLMs power chat apps like ChatGPT, but also they are increasingly underlying almost every aspect of AI. Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where SurrealDB Social is held. What's a SurrealDB event like? Check out photos from our previous events at https://surrealdb.gallery. Who are SurrealDB? SurrealDB is a modern cloud-native multi-model database that allows users and developers to focus on building their applications rather than architecting and managing their infrastructure with features like SurrealQL, Live Queries, Search, Change Data Capture, and ML. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information.