This is the link to register to attend online. To register to attend in person, please use (In Person) Building a robust data foundation for AI success The Teams link will be published on the right of this page for those who have registered.

18:30: Building a robust data foundation for AI success - Maryleen Amaizu 19:30 Data News and Roundup 19:45 Finish

Session details: Building a robust data foundation for AI success - Maryleen Amaizu In the age of artificial intelligence, the fuel that drives innovation is not just code, but data. But having a data lake isn't enough. Join us as we explore 5 critical foundations that transform raw data into the driving force behind impactful AI solutions:

Data Strategy: Setting a clear vision for how your data will empower AI initiatives. Data Preparation: From collection to cleansing, mastering the art of preparing clean, annotated, and privacy-sensitive data for AI consumption. Data Governance: Ensuring data quality, security, and compliance to build trust and avoid pitfalls. Scalability and Infrastructure: Building a robust architecture that can handle the ever-growing volume and velocity of data. Feedback Loops: Continuously improving your AI models by feeding them high-quality, relevant feedback data. Using a real-world case study, we’ll deep dive into how we can embed privacy engineering and compliance into data pipelines — automating sensitive data detection, de-identification, and risk assessment to enable responsible and ethical AI development. You’ll walk away with practical insights and strategies to build strong data foundations that balance innovation with privacy and trust.

Speaker: Maryleen Amaizu Machine Learning Engineer at Redgate Dr. Maryleen Amaizu is a Machine Learning Engineer at Redgate Software, specialising in synthetic data generation for the Test Data Management team. With a PhD and a strong background in machine learning and Internet of Things, she applies her expertise to develop innovative solutions that enhance data privacy and compliance in software testing. Previously, as a Principal Investigator at the Alan Turing Institute, she led a research project in collaboration with kunato.ai, focusing on building trust and combating misinformation with AI. Maryleen earned her PhD from the University of Leicester, where she designed performance-efficient, privacy-preserving machine learning systems for resource-constrained environments. Her global contributions to digital technology have been recognized with two Global Talent Visa Awards in the UK and Australia. She co-founded Glotale, a platform focused on attracting talents to booming locales and opportunities worldwide.

X:@maryleenamaizu ln: linkedin.com/in/maryleenamaizu

This is the link to register to attend in person. To register to attend online, please use (Online) Building a robust data foundation for AI success

18:00- 18:30 - Networking and Refreshments 18:30: Building a robust data foundation for AI success - Maryleen Amaizu 19:30: Roundup of data news

Session details: Building a robust data foundation for AI success - Maryleen Amaizu In the age of artificial intelligence, the fuel that drives innovation is not just code, but data. But having a data lake isn't enough. Join us as we explore 5 critical foundations that transform raw data into the driving force behind impactful AI solutions:

Data Strategy: Setting a clear vision for how your data will empower AI initiatives. Data Preparation: From collection to cleansing, mastering the art of preparing clean, annotated, and privacy-sensitive data for AI consumption. Data Governance: Ensuring data quality, security, and compliance to build trust and avoid pitfalls. Scalability and Infrastructure: Building a robust architecture that can handle the ever-growing volume and velocity of data. Feedback Loops: Continuously improving your AI models by feeding them high-quality, relevant feedback data. Using a real-world case study, we’ll deep dive into how we can embed privacy engineering and compliance into data pipelines — automating sensitive data detection, de-identification, and risk assessment to enable responsible and ethical AI development. You’ll walk away with practical insights and strategies to build strong data foundations that balance innovation with privacy and trust.

Speaker: Maryleen Amaizu Machine Learning Engineer at Redgate Dr. Maryleen Amaizu is a Machine Learning Engineer at Redgate Software, specialising in synthetic data generation for the Test Data Management team. With a PhD and a strong background in machine learning and Internet of Things, she applies her expertise to develop innovative solutions that enhance data privacy and compliance in software testing. Previously, as a Principal Investigator at the Alan Turing Institute, she led a research project in collaboration with kunato.ai, focusing on building trust and combating misinformation with AI. Maryleen earned her PhD from the University of Leicester, where she designed performance-efficient, privacy-preserving machine learning systems for resource-constrained environments. Her global contributions to digital technology have been recognized with two Global Talent Visa Awards in the UK and Australia. She co-founded Glotale, a platform focused on attracting talents to booming locales and opportunities worldwide.

X:@maryleenamaizu ln: linkedin.com/in/maryleenamaizu

This event is sponsored by Humand Talent Solutions, who provide the best technical talent for local start-ups and scale-ups, and Packt Books who provide the eBooks for the prize draw.

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.

In January the Synthetic Data SDK was introduced and it quickly is gaining traction as becoming the standard Open Source library for creating privacy-preserving synthetic data. In this hands-on tutorial we're going beyond the basics and we'll look at many of the advanced features of the SDK including differential privacy, conditional generation, multi-tables, and fair synthetic data.

Explore AI and Machine Learning fundamentals, tools, and applications in this beginner-friendly guide. Learn to build models in Python and understand AI ethics. Key Features Covers AI fundamentals, Machine Learning, and Python model-building Provides a clear, step-by-step guide to learning AI techniques Explains ethical considerations and the future role of AI in society Book Description This book is an ideal starting point for anyone interested in Artificial Intelligence and Machine Learning. It begins with the foundational principles of AI, offering a deep dive into its history, building blocks, and the stages of development. Readers will explore key AI concepts and gradually transition to practical applications, starting with machine learning algorithms such as linear regression and k-nearest neighbors. Through step-by-step Python tutorials, the book helps readers build and implement models with hands-on experience. As the book progresses, readers will dive into advanced AI topics like deep learning, natural language processing (NLP), and generative AI. Topics such as recommender systems and computer vision demonstrate the real-world applications of AI technologies. Ethical considerations and privacy concerns are also addressed, providing insight into the societal impact of these technologies. By the end of the book, readers will have a solid understanding of both the theory and practice of AI and Machine Learning. The final chapters provide resources for continued learning, ensuring that readers can continue to grow their AI expertise beyond the book. What you will learn Understand key AI and ML concepts and how they work together Build and apply machine learning models from scratch Use Python to implement AI techniques and improve model performance Explore essential AI tools and frameworks used in the industry Learn the importance of data and data preparation in AI development Grasp the ethical considerations and the future of AI in work Who this book is for This book is ideal for beginners with no prior knowledge of AI or Machine Learning. It is tailored to those who wish to dive into these topics but are not yet familiar with the terminology or techniques. There are no prerequisites, though basic programming knowledge can be helpful. The book caters to a wide audience, from students and hobbyists to professionals seeking to transition into AI roles. Readers should be enthusiastic about learning and exploring AI applications for the future.

Are you a cybersecurity professional looking to connect with like-minded professionals, share experiences, and make friends? Look no further! Join us for a special edition of the Berlin Cybersecurity Social hosted in collaboration with the Venture Café Berlin and the AI Ethics Action Hub for a fantastic evening of networking.

Agenda:

• 5:00 PM - 5:15 PM: Welcome
• 5:15 PM – 5:50 PM: Lightning Talk: AI Threat Modeling: how to bring the right mindset to detect and prevent AI risk - Iryna Schwindt In this talk, we'll explore the full spectrum of AI risks—not just security-related ones—and why understanding the application context is critical. You'll learn: - What are the types of AI risks (not only security risks) - Why AI application context matters - How to identify potential threats\, apply effective controls and guardrails - Cultivating the right mindset to detect and prevent AI risks *
• 5:50 PM - 6:30 PM: Panel: AI Meets Cybersecurity: Building Smarter, Safer Systems at Scale - Jose Quesada, Diana Waithanji, Ali Yazdani, Pranav Vattaparambil As AI rapidly integrates into every layer of digital infrastructure, the stakes for cybersecurity have never been higher. This panel brings together experts from across the security spectrum—ranging from DevSecOps and enterprise risk to cybersecurity strategy—to explore how AI is transforming threat detection, governance, and secure system design. We’ll dive into real-world use cases, emerging risks, and what it takes to build scalable, intelligent, and secure systems in an increasingly AI-driven world.
• 6:30 PM - 8:00 PM: Breakout Session: Cybersecurity in the Age of AI: Ethics & Human-Centered Future *Featuring Azer Aliyev (speakinprivate.com), Gunay Kazimzade (Mercedes-Benz Consulting), and Justin Shenk (AI Salon Berlin), this fast-paced session brings together innovators, researchers, and tech leaders to explore how to build AI systems that protect privacy, bolster trust, and keep humans at the heart of digital transformation.

*This session is organised by the AI Ethics Action Hub

About the Speakers:

Iryna Schwindt is a Cybersecurity engineer currently at Vodafone and a co-author at the OWASP AI Exchange (https://owaspai.org/) project, contributing to the EU AI Act security standard and AI Red Teaming.

Jose Quesada is the founder and director of Data Science Retreat (DSR), an advanced ML bootcamp that has helped over 300 professionals land data science roles. With a PhD and 20+ years in machine learning, Jose brings a unique blend of technical depth and creative flair—he’s also a former photorealism artist. He has advised on impactful projects ranging from malaria diagnostics to sustainability-focused robotics.

Diana Waithanji is a Cybersecurity Engineer at SAP SE, with experience working across Europe and Africa. She is an advocate for data privacy as a fundamental human right and serves on two technical committees at the Kenya Bureau of Standards. Diana is also a board member at Nivishe Foundation, where she supports youth mental health through safe spaces. Her work bridges global standards, social impact, and cutting-edge security practices.

Ali Yazdani is a seasoned security professional with over a decade of experience spanning offensive security and secure development practices. Starting his career as a penetration tester, he now specializes in building scalable DevSecOps programs and embedding security into engineering workflows. Ali brings deep technical knowledge and a pragmatic approach to security culture. His mission is to empower teams to build safer software at scale and is currently a founder at Scandog.io

Pranav Vattaparambil is Chief Security Officer at Unosecur (https://www.unosecur.com/) as well as a security and product strategist with deep expertise in fintech. Formerly VP of Cybersecurity at the EU’s largest Banking-as-a-Service company, he also advises multiple startups on navigating security, risk, and go-to-market strategy. Pranav bridges the gap between technical execution and business impact, especially in regulated industries like banking and crypto. His focus is on helping companies build secure, scalable products from day one.

About Venture Café Berlin: Venture Café Berlin connects a community of innovators and entrepreneurs with free high-impact programming and events. Venture Café is a part of the CIC network, whose mission is to fix the world through innovation.

About Berlin Cybersecurity Social: This meetup is open to cybersecurity professionals of all levels, from beginners to experts. Whether you're a seasoned pro or just starting your journey in the field, this event is the perfect opportunity to connect with others who share your passion for cybersecurity.

About the AI Ethics Action Hub: A global, interdisciplinary collective dedicated to advancing ethical, inclusive, and accountable AI. We believe technology should be designed to respecting human dignity, planetary well-being, and intergenerational justice.

GBA Public Webinar "Credentialing Without Compromise" 12 noon EDT Wednesday, July 23rd as part of GBA's Healthcare & Life Sciences Monthly Webinar Series Government Blockchain Association's Healthcare & Life Science Month Public Webinar on "GBA Healthcare Life Science Webinar: Credentialing Without Compromise: How Blockchain Secures and Streamlines Healthcare Identity" July 23rd, Wednesday 12 noon to 1 pm

Join us for the Government Blockchain Association's (GBA) Healthcare & Life Sciences (HLS) Monthly Webinar Series, taking place the 4th Wednesday of every month from 12-1 pm EDT.

Each month delivers a deep-dive into a specific topic or theme being taken up by the HLS Working Group.

Join the GBA HLS Webinar at https://us06web.zoom.us/webinar/register/WN_r8DP_2fMSqeFd2kg0MUksA#/registration Webinar ID 846 9107 1362 Passcode 563973

GBA Healthcare Life Science Webinar: Credentialing Without Compromise: How Blockchain Secures and Streamlines Healthcare Identity.

Explore how blockchain transforms healthcare credentialing from a fragmented, manual burden into a fast, verifiable, and sovereign system.

Understand how decentralized identity frameworks enhance compliance, reduce fraud, and empower professionals to own and manage their credentials across institutions. Learn how Vannadium's data infrastructure supports real-time credential validation without replacing existing EHRs or core systems.

Vannadium executives provide practical insights from on sovereign data strategies for healthcare: Walt Rampata, Vannadium Co-Founder &Exec VP Laura Fredericks, Vannadium Chief Marketing Officer

Walt Rampata, EVP & Co-Founder·Vannadium Walt Rampata focuses on driving company growth. With over two decades of professional experience, his career is marked by a consistent record of developing successful brands, building effective teams, and increasing revenue. A seasoned entrepreneur and marketing expert, Rampata has a history of successful ventures, including serving as CEO & Co-Founder of WeGro and President and Co-Founder of Mindbuzz. His pragmatic approach is centered on achieving measurable bottom-line results.In his current role at Vannadium, Rampata leverages his extensive experience to advance the understanding and adoption of Blockchain, Web 3.0, and Distributed Ledger Technologies. He is focused on educating both consumers and industries on how these technologies can solve critical challenges.

Laura Fredericks, Vannadium Chief Marketing Officer Laura Fredericks is a marketing executive and serial entrepreneur specializing in the technology sector. In her capacity as Chief Marketing Officer (CMO) at Vannadium, she directs the company's growth and marketing strategies. Fredericks has founded four separate companies, providing her with a comprehensive understanding of the entire business lifecycle, from conception to scaling. Her experience is not limited to an advisory role; she has been directly involved in the operational and strategic challenges of building businesses. Her tenure as the head of Consumer Product Marketing at Kraken Digital Asset Exchange demonstrates her capability within the financial technology and cryptocurrency markets. At Kraken, she led initiatives that were directly responsible for increasing transactional uplift by over $60 million. This was achieved through targeted consumer marketing campaigns and product marketing strategies. Her skill set includes market expansion, brand development, and strategic growth planning for technology-focused organizations.

Hosted by Heather Leigh Flannery Chair, GBA HLS Working Group Heather Leigh Flannery is an applied futurist, technologist, policy and patient advocate, and complex systems theorist. She co-founded AI MINDSystems Foundation in March 2024 with Chief Scientific Officer, Sean Manion, PhD, and other leaders with a timely vision for structural interventions for humanity's health, safety, prosperity, and privacy, and serves as CEO. Heather also Chairs the Healthcare & Life Sciences Working Group at the Government Blockchain Association (GBA) and the Washington, DC Chapter of AI 2030, and develops standards at IEEE.

About GBA The Government Blockchain Association (GBA) is a nonprofit (501c6) organization committed to advancing blockchain technology standards, facilitating industry education, and ensuring a trusted, secure ecosystem for blockchain solutions. For more information, visit https://gbaglobal.org/.

Join the GBA HLS Webinar at https://us06web.zoom.us/webinar/register/WN_r8DP_2fMSqeFd2kg0MUksA#/registration Webinar ID 846 9107 1362 Passcode 563973

For more information contact Bob Miko, [email protected] 203 378 2803 Bob Miko GBA Director of Public Relations Editor in Chief/Producer Pacific Dialogue 203 378 2803 [email protected]

GBA Public Webinar "Credentialing Without Compromise" 12 noon EDT Wednesday, June 25th as part of GBA's Healthcare & Life Sciences Monthly Webinar Series Government Blockchain Association's Healthcare & Life Science Month Public Webinar on "GBA Healthcare Life Science Webinar: Credentialing Without Compromise: How Blockchain Secures and Streamlines Healthcare Identity" June 25th, Wednesday 12 noon to 1pm Join us for the Government Blockchain Association's (GBA) Healthcare & Life Sciences (HLS) Monthly Webinar Series, taking place the 4th Wednesday of every month from 12-1 pm EDT. Each month delivers a deep-dive into a specific topic or theme being taken up by the HLS Working Group.

Join the GBA HLS Webinar at https://us06web.zoom.us/webinar/register/WN_r8DP_2fMSqeFd2kg0MUksA#/registration

GBA Healthcare Life Science Webinar: Credentialing Without Compromise: How Blockchain Secures and Streamlines Healthcare Identity. Explore how blockchain transforms healthcare credentialing from a fragmented, manual burden into a fast, verifiable, and sovereign system. Understand how decentralized identity frameworks enhance compliance, reduce fraud, and empower professionals to own and manage their credentials across institutions. Learn how Vannadium's data infrastructure supports real-time credential validation without replacing existing EHRs or core systems. Vannadium executives provide practical insights from on sovereign data strategies for healthcare: Walt Rampata, Vannadium Co-Founder &Exec VP Laura Fredericks, Vannadium Chief Marketing Officer

Walt Rampata, EVP & Co-Founder·Vannadium Walt Rampata focuses on driving company growth. With over two decades of professional experience, his career is marked by a consistent record of developing successful brands, building effective teams, and increasing revenue. A seasoned entrepreneur and marketing expert, Rampata has a history of successful ventures, including serving as CEO & Co-Founder of WeGro and President and Co-Founder of Mindbuzz. His pragmatic approach is centered on achieving measurable bottom-line results.In his current role at Vannadium, Rampata leverages his extensive experience to advance the understanding and adoption of Blockchain, Web 3.0, and Distributed Ledger Technologies. He is focused on educating both consumers and industries on how these technologies can solve critical challenges.

Laura Fredericks, Vannadium Chief Marketing Officer Laura Fredericks is a marketing executive and serial entrepreneur specializing in the technology sector. In her capacity as Chief Marketing Officer (CMO) at Vannadium, she directs the company's growth and marketing strategies. Fredericks has founded four separate companies, providing her with a comprehensive understanding of the entire business lifecycle, from conception to scaling. Her experience is not limited to an advisory role; she has been directly involved in the operational and strategic challenges of building businesses. Her tenure as the head of Consumer Product Marketing at Kraken Digital Asset Exchange demonstrates her capability within the financial technology and cryptocurrency markets. At Kraken, she led initiatives that were directly responsible for increasing transactional uplift by over $60 million. This was achieved through targeted consumer marketing campaigns and product marketing strategies. Her skill set includes market expansion, brand development, and strategic growth planning for technology-focused organizations.

Hosted by Heather Leigh Flannery Chair, GBA HLS Working Group Heather Leigh Flannery is an applied futurist, technologist, policy and patient advocate, and complex systems theorist. She co-founded AI MINDSystems Foundation in March 2024 with Chief Scientific Officer, Sean Manion, PhD, and other leaders with a timely vision for structural interventions for humanity's health, safety, prosperity, and privacy, and serves as CEO. Heather also Chairs the Healthcare & Life Sciences Working Group at the Government Blockchain Association (GBA) and the Washington, DC Chapter of AI 2030, and develops standards at IEEE.

About GBA The Government Blockchain Association (GBA) is a nonprofit (501c6) organization committed to advancing blockchain technology standards, facilitating industry education, and ensuring a trusted, secure ecosystem for blockchain solutions. For more information, visit https://gbaglobal.org/.

Join the GBA HLS Webinar at https://us06web.zoom.us/webinar/register/WN_r8DP_2fMSqeFd2kg0MUksA#/registration

For more information contact Bob Miko, [email protected] 203 378 2803

Bob Miko GBA Director of Public Relations Editor in Chief/Producer Pacific Dialogue 203 378 2803 [email protected]

This session will focus on the security aspects of Databricks Delta Sharing, Databricks Cleanrooms and Databricks Marketplace, providing an exploration of how these solutions enable secure and scalable data collaboration while prioritizing privacy. Highlights: Use cases — Understand how Delta Sharing facilitates governed, real-time data exchange across platforms and how Cleanrooms support multi-party analytics without exposing sensitive information Security internals — Dive into Delta Sharing's security frameworks Dynamic views — Learn about fine-grained security controls Privacy-first Cleanrooms — Explore how Cleanrooms enable secure analytics while maintaining strict data privacy standards Private exchanges — Explore the role of private exchanges using Databricks Marketplace in securely sharing custom datasets and AI models with specific partners or subsidiaries Network security & compliance — Review best practices for network configurations and compliance measures

GBA Public Webinar "Blockchain Injects Trust & Assurance into Health Data, Analytics & Clinical AI" 12 noon EDT Wednesday, April 23rd as part of GBA's Healthcare & Life Sciences Monthly Webinar Series

Government Blockchain Association's Healthcare & Life Science Month Public Webinar on "How Blockchain is Injecting Trust & Assurance into Health Data, Analytics & Clinical AI" April 23, Wednesday 12 noon to 1pm

Join us for the Government Blockchain Association's (GBA) Healthcare & Life Sciences (HLS) Monthly Webinar Series, taking place the 4th Wednesday of every month from 12-1pm EST. Each month will deliver a deep-dive into a specific topic or theme being taken up by the HLS Working Group.

Join the Webinar at https://us06web.zoom.us/webinar/register/WN_r8DP_2fMSqeFd2kg0MUksA or https://rb.gy/26ocq9.

Clinical Squared Chief Executive Officer Marquis Allen will discuss Blockchain utility for Healthcare enterprise & government, blockchain identity opportunities and where AI projects that benefit from a blockchain layer.

Heather Leigh Flannery Chair\, GBA HLS Working Group \| Host\, Monthly HLS Livestream Series· Government Blockchain Association Heather Leigh Flannery is an applied futurist, technologist, policy and patient advocate, and complex systems theorist. She co-founded AI MINDSystems Foundation in March 2024 with Chief Scientific Officer, Sean Manion, PhD, and other leaders with a timely vision for structural interventions for humanity's health, safety, prosperity, and privacy, and serves as CEO. Heather also Chairs the Healthcare & Life Sciences Working Group at the Government Blockchain Association (GBA) and the Washington, DC Chapter of AI 2030, and develops standards at IEEE.

Marquis Allen Chief Executive Officer Clinical Squared As an IT professional for the past 20+ years, Marquis has had the great opportunity to work with many innovative organizations. When he transitioned into the clinical IT space, his fascination for the possibilities of what technology could do was piqued by learning about the complex problems that clinical practices face in leveraging technology to care for patients in the 21st century. Blockchain and AI took center stage, and form the foundations of differentiating value Clinical Squared is bringing the US Federal and state government and commercial clients.

About GBA

The Government Blockchain Association (GBA) is a nonprofit (501c6) organization committed to advancing blockchain technology standards, facilitating industry education, and ensuring a trusted, secure ecosystem for blockchain solutions. For more information, visit https://gbaglobal.org/.

For more information contact Bob Miko, [email protected] 203 378 2803

-- Bob Miko GBA Director of Public Relations Editor in Chief/Producer Pacific Dialogue 203 378 2803 [email protected]

AI is transforming industries, but it’s also raising complex questions about data protection and privacy. EDPB Opinion 28/204 provides guidance specifically for GDPR practitioners dealing with AI.

00:00 Introduction to AI and GDPR 00:33 Understanding Anonymity in AI Models 01:53 Framework for Determining Anonymity 03:30 Practical Steps for GDPR Compliance 06:16 Exploring Legitimate Interests 07:19 The Three-Step Test for Legitimate Interests 10:18 Navigating Legitimate Interests 10:34 Understanding the Balancing Test 11:17 Risks and Rights in AI Data Processing 14:59 Mitigating Measures for Data Protection 17:16 Web Scraping and Data Protection 18:24 Consequences of Unlawful Data Processing 20:13 Key Takeaways for GDPR Practitioners

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. This week, Yannick joins the conversation for a lively year-end retrospective on the state of AI, data, and technology in 2024. Whether you're knee-deep in neural networks or just data-curious, this episode offers plenty to ponder. Grab your coffee, sit back, and explore: AI’s meteoric rise in 2024: How GenAI went from hype to tangible business tools and what’s ahead for 2025.Strategic AI adoption: Challenges and best practices for embedding AI into workflows and decision-making processes.Real-time data: From dynamic pricing to e-commerce triggers, we explore gaps and future trends in event-driven infrastructure.The ethics and compliance puzzle: A dive into the EU AI Act, data privacy, and the evolving landscape of ethical AI usage.Developer tools and trends: Productivity boosters like Copilot and the rise of tools like PDM and Ubi in the Python ecosystem.With reflections on everything from Lakehouse data platforms to open-source debates, this episode is the perfect blend of geeky insights and forward-looking predictions. Pull up a chair, relax, and let’s dive into the world of data, unplugged style!

PLEASE SIGN UP HERE: https://lu.ma/g1qtibmf

Hello Community, We're excited to announce our next meetup! ​Date: November 13th ​Time: 6:00 PM onwards ​Venue: W3 HUB ​Join us for a deep dive into the game-changing world of Compound AI Systems—the next frontier in AI innovation. These modular AI systems are revolutionizing industries with their ability to handle complex, real-world tasks by combining specialized models ​🎤 Featured Speakers ​• Valdimar Eggertsson - AI Team Lead @ Snjallgögn (Smart Data Inc.) ​• Aravind Segu - Software Engineer @ Databricks ​• Daryl Ngo - AI Architect @ Arize AI ​Network over drinks and snacks! ​--- ​🔒 Code of Conduct: Please familiarize yourself with our Code of Conduct before attending the event. We strive to create an inclusive and respectful environment for all participants. By joining us, you agree to abide by the guidelines outlined in our Code of Conduct. You can find it here. ​​​📷 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 before or during the event. Your comfort and privacy are important to us. ​

Hello Community

We're excited to announce our next meetup!

Date: November 13th Time: 6:00 PM onwards Venue: W3 HUB

Join us for a deep dive into the game-changing world of Compound AI Systems—the next frontier in AI innovation. These modular AI systems are revolutionizing industries with their ability to handle complex, real-world tasks by combining specialized models

🎤 Featured Speakers:

• Valdimar Eggertsson - AI Team Lead @ Snjallgögn (Smart Data Inc.)

• Aravind Sigu - Software Engineer @ Databricks

• Daryl Ngo - AI Architect @ Arize AI

Network over drinks and snacks! --- 🔒 Code of Conduct: Please familiarize yourself with our Code of Conduct before attending the event. We strive to create an inclusive and respectful environment for all participants. By joining us, you agree to abide by the guidelines outlined in our Code of Conduct. You can find it here. ​​📷 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 before or during the event. Your comfort and privacy are important to us.

MachineLearning #MeetupEvent #BerlinTech #MLOPS