talk-data.com

Build a Multi-Agent Deep Research System with Temporal - Alexey Grigorev

​In this hands-on workshop, you'll build a durable deep-research agent and learn how to make LLM-powered systems reliable enough for real production environments.

​We’ll walk through:

• ​Building a deep-research agent step by step
• ​Handling failures and unpredictable LLM behavior
• ​Designing agentic workflows with Temporal + PydanticAI

​By the end of the workshop, you'll know how to take an idea from PoC to a production-grade multi-agent system with Temporal: observable, fault-tolerant, easy to extend, and designed to survive real-life conditions. ​ ​About the speaker:

​Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.

​Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge. ​

​​​​​​​DataTalks.Club is the place to talk about data. Join our slack community! ​

​This event is sponsored by Temporal.

❄️ DSF WinterFest 2025: Global Online Summit ❄️

Join the global data celebration!

Monday 24th to Friday 28th November 2025 Online \| 2-3 sessions per day \| Theme: Innovating with Data

DSF WinterFest is back, and this year, it’s going global! Join our 50,000-strong community for a week of world-class talks, tutorials, and panels exploring how data, AI, and analytics are reshaping the world. Expect inspiring content, expert insights, and the cosy, welcoming DSF atmosphere we are known for, all from the comfort of your own space!

Why join? 🌍 A global stage with speakers and attendees from every corner of the world 🎟️ One ticket for the full week. Register once and access every session 💻 Easy access from anywhere. Join live or catch replays in your own time ☕ Cosy community vibe. No travel, no stress, just data and connection

➡️ REGISTER HERE FOR FREE! ⬅️

🎟️ Tickets:

Choose your experience and secure your spot today: Free Pass - Watch live and enjoy replays until 30 November 2025 Upgrade at Checkout - Get extended replay access until May 2026

Register on our website to receive your joining links, add sessions to your calendar, and tune in live from anywhere in the world.

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

🎁 Competition:

We’re spreading festive cheer! One lucky attendee will win a £300 Amazon gift voucher (or equivalent in your currency). Find out more here.

❄️❄️❄️

Session details:

💡 Context-Aware Vision Systems Using Knowledge Graphs 🗓️ Tuesday 25th November ⏰ 15:00 PM GMT 🗣️ Niyati P, Software, ML Lead

Traditional computer vision systems often rely solely on pixel-level features and deep learning to interpret images, limiting their ability to understand complex scenes or generalize across contexts. This talk introduces a new paradigm: context-aware vision systems powered by knowledge graphs. By embedding structured semantic knowledge into vision pipelines, machines can infer not just what is seen, but why it matters. We explore how knowledge graphs provide contextual cues—such as object relationships, scene hierarchies, and domain-specific constraints—that enhance visual perception and reasoning. From improving object recognition in ambiguous environments to enabling semantic scene parsing and zero-shot learning, this approach bridges the gap between raw visual data and high-level cognition. The session will highlight recent breakthroughs, implementation strategies using Graph Neural Networks (GNNs), and applications in domains like autonomous driving, medical imaging, and industrial robotics. Attendees will leave with insights on designing vision systems that not only see—but understand.

➡️ REGISTER HERE FOR FREE! ⬅️

❄️❄️❄️

🔗 How to join:

Once registered, you’ll receive your unique joining link by email, plus handy reminders one week, one day, and one hour before each session. Don't forget to add the sessions you are attending to your calendar. If you can’t make it live, don’t worry, your ticket includes replay access until 30 November 2025 (or May 2026 with the upgrade).

📘 Reminders:

Time zones: All sessions are listed in GMT - please check your local time when registering.

Recordings: Access replays until 30 November 2025 with a free pass, or until May 2026 with an upgraded ticket

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

➡️ REGISTER HERE FOR FREE! ⬅️

Join the Celebration ❄️ Five days. Global speakers. Cutting-edge insights. Free to join live - replays included. Upgrade for extended access. Register now and be part of the global data community shaping the future. #DSFWinterFest

Summary In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Matt Topper about the challenges of managing identity and access controls in the context of data systemsInterview IntroductionHow did you get involved in the area of data management?The data ecosystem is a uniquely challenging space for creating and enforcing technical controls for identity and access control. What are the key considerations for designing a strategy for addressing those challenges?For data acess the off-the-shelf options are typically on either extreme of too coarse or too granular in their capabilities. What do you see as the major factors that contribute to that situation?Data governance policies are often used as the primary means of identifying what data can be accesssed by whom, but translating that into enforceable constraints is often left as a secondary exercise. How can we as an industry make that a more manageable and sustainable practice?How can the audit trails that are generated by data systems be used to inform the technical controls for identity and access?How can the foundational technologies of our data platforms be improved to make identity and authz a more composable primitive?How does the introduction of streaming/real-time data ingest and delivery complicate the challenges of security controls?What are the most interesting, innovative, or unexpected ways that you have seen data teams address ICAM?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ICAM?What are the aspects of ICAM in data systems that you are paying close attention to?What are your predictions for the industry adoption or enforcement of those controls?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links UberEtherJWT == JSON Web TokenOPA == Open Policy AgentRegoPingIdentityOktaMicrosoft EntraSAML == Security Assertion Markup LanguageOAuthOIDC == OpenID ConnectIDP == Identity ProviderKubernetesIstioAmazon CEDAR policy languageAWS IAMPII == Personally Identifiable InformationCISO == Chief Information Security OfficerOpenTDFOpenFGAGoogle ZanzibarRisk Management FrameworkModel Context ProtocolGoogle Data ProjectTPM == Trusted Platform ModulePKI == Public Key InfrastructurePassskeysDuckLakePodcast EpisodeAccumuloJDBCOpenBaoHashicorp VaultLDAPThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this episode, we talked with Ranjitha Kulkarni, a machine learning engineer with a rich career spanning Microsoft, Dropbox, and now NeuBird AI. Ranjitha shares her journey into ML and NLP, her work building recommendation systems, early AI agents, and cutting-edge LLM-powered products. She offers insights into designing reliable AI systems in the new era of generative AI and agents, and how context engineering and dynamic planning shape the future of AI products.TIMECODES00:00 Career journey and early curiosity04:25 Speech recognition at Microsoft05:52 Recommendation systems and early agents at Dropbox07:44 Joining NewBird AI12:01 Defining agents and LLM orchestration16:11 Agent planning strategies18:23 Agent implementation approaches22:50 Context engineering essentials30:27 RAG evolution in agent systems37:39 RAG vs agent use cases40:30 Dynamic planning in AI assistants43:00 AI productivity tools at Dropbox46:00 Evaluating AI agents53:20 Reliable tool usage challenges58:17 Future of agents in engineering Connect with Ranjitha- Linkedin - https://www.linkedin.com/in/ranjitha-gurunath-kulkarniConnect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack. Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer — systems thinking is.

We'll use concrete examples to walk through common failure modes in ML pipelines, highlight where analogies fall apart, and show how to build systems that tolerate failure, adapt to change, and support iteration without regressions.

Topics covered include: - Common failure modes in ML pipelines - Modular design: feature, training, inference - Built-in observability, versioning, reuse - Orchestration across batch, real-time, LLMs - Platform-agnostic patterns that scale

Key takeaways: - Resilience > diagrams - Separate concerns, embrace change - Metadata is your backbone - Infra should support iteration, not block it

Summary In this crossover episode of the AI Engineering Podcast, host Tobias Macey interviews Brijesh Tripathi, CEO of Flex AI, about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. Join them as they discuss Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.

Pre-amble I hope you enjoy this cross-over episode of the AI Engineering Podcast, another show that I run to act as your guide to the fast-moving world of building scalable and maintainable AI systems. As generative AI models have grown more powerful and are being applied to a broader range of use cases, the lines between data and AI engineering are becoming increasingly blurry. The responsibilities of data teams are being extended into the realm of context engineering, as well as designing and supporting new infrastructure elements that serve the needs of agentic applications. This episode is an example of the types of work that are not easily categorized into one or the other camp.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterview IntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact Info LinkedInParting Question From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Links Flex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this episode of Experiencing Data, I introduce part 1 of my new MIRRR UX framework for designing trustworthy agentic AI applications—you know, the kind that might actually get used and have the opportunity to create the desired business value everyone seeks! One of the biggest challenges with both traditional analytics, ML, and now, LLM-driven AI agents, is getting end users and stakeholders to trust and utilize these data products—especially if we’re asking humans in the loop to make changes to their behavior or ways of working. 

In this episode, I challenge the idea that software UIs will vanish with the rise of AI-based automation. In fact, the MIRRR framework is based on the idea that AI agents should be “in the human loop,” and a control surface (user interface) may in many situations be essential to ensure any automated workers engender trust with their human overlords.  

By properly considering the control and oversight that end users and stakeholders need, you can enable the business value and UX outcomes that your paying customers, stakeholders, and application users seek from agentic AI. 

Using use cases from insurance claims processing, in this episode, I introduce the first two of five control points in the MIRRR framework—Monitor and Interrupt. These control points represent core actions that define how AI agents often should operate and interact within human systems:

Monitor – enabling appropriate transparency into AI agent behavior and performance Interrupt – designing both manual and automated pausing mechanisms to ensure human oversight remains possible when needed

…and in a couple weeks, stay tuned for part 2 where I’ll wrap up this first version of my MIRRR framework. 

Highlights / Skip to:

00:34 Introducing the MIRRR UX Framework for designing trustworthy agentic AI Applications.  01:27 The importance of trust in AI systems and how it is linked to user adoption 03:06 Cultural shifts, AI hype, and growing AI skepticism 04:13  Human centered design practices for agentic AI   06:48 I discuss how understanding your users’ needs does not change with agentic AI, and that trust in agentic applications has direct ties to user adoption and value creation 11:32 Measuring success of agentic applications with UX outcomes 15:26 Introducing the first two of five MIRRR framework control points: 16:29 M is for Monitor; understanding the agent’s “performance,” and the right level of transparency end users need, from individual tasks to aggregate views  20:29 I is for Interrupt; when and why users may need to stop the agent—and what happens next

28:02 Conclusion and next steps

In our upcoming meetup, we'll explore best practices in MLOps, ensuring robust and automated workflows, and discuss the latest advancements in Generative AI for real-world applications. Whether you're optimizing data pipelines, scaling AI models, or navigating the transition from experimentation to production, this event will provide valuable insights from industry experts.

Between presentations, you will have the opportunity of networking and meeting data enthusiasts at the Netlight office, food and drinks will be served.

Agenda:

17:30 - 18:00: Doors open 18:00 - 18:10: Welcome 18:10 - 18:40: Navigating the Intersection of MLOps and GenAI: A Comparative Exploration 18:40 - 19:10: Break 19:10 - 19:40: Building LEGO Castles Instead of Sandcastles: A Tale of Modularity in Data & ML Systems 19:40 - 20:30: Networking

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Presentations:

Navigating the Intersection of MLOps and GenAI: A Comparative Exploration Per Hedbrant - Consultant, Netlight Martti Yap - Consultant, Netlight

In this presentation, we'll embark on a journey through the evolving landscapes of MLOps and GenAI architectures. Drawing from extensive experience in data engineering and machine learning, coupled with hands-on work in the emerging field of GenAI, we will provide insights into the fundamental differences and similarities between these two domains.

We'll delve into the core components of a mature MLOps platform, highlighting processes like data preparation, model training, and deployment. Then, we'll contrast these with the emerging architecture of GenAI, exploring concepts of observability, guardrails, and model evaluation techniques.

This talk aims to equip you with a deeper understanding of where the focus lies in MLOps—emphasizing operational efficiency and model lifecycle management—and in GenAI—highlighting the demands of AI-driven solutions in production. Whether you're a student, a newly minted professional, or a seasoned expert, this session will provide valuable perspectives on integrating these technologies into your workflow, fostering both operational robustness and creative AI capabilities.

Speakers Bio: Per Hedbrant is a Netlight consultant with a strong background in data engineering and machine learning, currently engaged in advancing Generative AI solutions. Passionate about bridging the gap between traditional ML operations and cutting-edge AI innovations, Per is dedicated to unleashing business value through building AI products and teams.

Martti Yap is a Netlight consultant, with a background in data science and ML. He is currently developing generative AI capacities for industry enterprises. Martti thrives best where evolving business needs meet advanced technological solutions. He enjoys sparking interest and promoting knowledge sharing throughout organizations and teams.

Building LEGO Castles Instead of Sandcastles: A Tale of Modularity in Data & ML Systems Anton Gollbo - Data/ML Engineer, Netlight

Building reliable and scalable machine learning systems is challenging, especially when workflows rely on fragile, tightly coupled scripts and notebooks. These "sandcastle-like" systems—where every component depends on the exact state of the whole—break easily, slowing down iteration and making debugging painful. Without clear modularity, small changes can cause unintended failures, leading to rigid, hard-to-maintain pipelines that don't scale well. To address this, we shift towards a LEGO-like approach, where ML systems are built from small, interchangeable, and testable components. By designing modular pipelines with well-defined boundaries—such as independent data processing, feature engineering, model training, and evaluation steps—our goal is to create flexible and reusable workflows. This talk explores the journey from tightly coupled systems to composable architectures, showing how modular design enables faster iteration, greater reliability, and long-term scalability in ML development.

Speakers Bio: Anton is a consultant at Netlight, bringing extensive experience from data and machine learning projects. His professional journey has taken him through various stages of the data and ML lifecycle, cultivating an interest in constructing systems that are both data-intensive and designed for easy testing and modularity.

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About the event

Date: March 27th , 17:30 - 20:30 Location: Netlight Consulting AB, Regeringsgatan 25, 111 53 Stockholm. Directions: At the entrance, take the staircase and you will find the reception desk where one of the hosts will welcome you and give more information about the venue. Tickets: Sign up required. Anyone who is not on the list will not get in. The event is free of charge. Capacity: Space is limited to 100 participants. If you are signed up but unable to attend, please change your RSVP by March 26th. Food and drinks: Food and drinks will be provided. Questions: Please contact the meetup organizers.

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Code of Conduct The NumFOCUS Code of Conduct applies to this event; please familiarize yourself with it before attending. If you have any questions or concerns regarding the Code of Conduct, please contact the organizers.

About this session: AI-powered chatbots have evolved beyond simple question-answer interactions. Today, intelligent multi-agent systems are revolutionizing industries by working collaboratively, making autonomous decisions, and dynamically adapting to complex environments. In this session, we will explore: - What makes a true multi-agent system? - How Azure AI & AutoGen enable intelligent collaboration - Live Demo: Multi-Agent AI for Automated Decision-Making - Best Practices for Designing AI Agents that Adapt & Evolve By the end, attendees will gain hands-on insights into how to build, optimize, and scale intelligent multi-agent frameworks using Azure AI.

Who is it aimed at? - AI & Machine Learning Professionals - AI Engineers & ML Engineers – Want to build AI-driven multi-agent systems. - Data Scientists – Interested in LLMs, reinforcement learning, and AI workflows. - Azure AI Developers – Looking to integrate AutoGen & Azure OpenAI in real-world applications.

Why should I attend? If you’re interested in AI-driven automation, intelligent agents, and real-world AI applications beyond chatbots, this session is for you!

Learn more about AutoGen https://aka.ms/Mar25Autogen3

​Due to the BVG strike, we will start the event later at 19:00. Please don't worry about being late and join us if you can!

You’ve probably heard a lot about AI—how it’s changing industries, making decisions, and even shaping our daily lives. But did you know that AI can also be biased? Join us for two insightfult talks about bias in AI and tools like Sweet Summer Child Score that can help mitigate the harm.

**IMPORTANT** Please sign-up via Luma on Ape Unit's event page: https://lu.ma/e636qkuc?utm_source=eit

✍️ Event description This meetup is hosted by Empowered in Tech together with Ape Unit. Our goal for this event is to make the topic of bias in AI more accessible to everyone—regardless of background or expertise. We’ll explore what AI bias is, the real-world impact it has, and what we can do to reduce its harm. You don’t need to be an expert or work in tech—all perspectives are welcome!

Talk: Sweet Summer Child Score Sweet Summer Child Score is an open-source library to identify potential AI harms. A truism in tech is that we're good at asking "can we do it", but not "should we do it". Attempting to tackle the latter, this library offers a system scan to quickly identify potential harms, and build the capability of relative risk assessment.

SSCS does not explore the specifics of your stack or technical implementation -- instead it takes a step back to look at the ecosystem your technology will be deployed in, and the implementation choices which define the seam between your system and the broader world. Put simply, this is an attempt to see the forest, not the trees. Links: The project and GitHub repos are online at https://summerchild.dev

[SECOND TALK CANCELLED: Talk: Exploring fairlearn and practical strategies for assessing and mitigating harm in AI systems As AI becomes a more significant part of our everyday lives, ensuring these systems are fair is more important than ever. In this session, we’ll discuss how to define fairness and the potential harms our algorithms can have on people and society. We’ll introduce fairlearn, a community-driven, open-source project that offers practical tools for assessing and mitigating harm in AI systems. We’ll also explore how to discuss bias, different types of harm, the idea of group fairness and how they all relate to fairlearn’s toolkit. To make it all concrete, we’ll walk through a real-world example of assessing fairness and share some hands-on strategies you can use to mitigate harm in your own ML projects. Links*:* - website: https://fairlearn.org/ - repository: https://github.com/fairlearn/fairlearn - LinkedIn: https://www.linkedin.com/company/fairlearn/]

🏢 Location We are kindly hosted by Ape Unit.

📝 Sign Up This time the sign-up is handled by our host Ape Unit via Luma: https://lu.ma/e636qkuc?utm_source=eit

⏰ Agenda 18:30 Doors open, we'll mingle and have some snacks 18:45 Intro and welcome from your hosts 19:00 Talk by Laura: Sweet Summer Child Score (+ Q&A) 19:45 Time for Networking 21: 15 Doors close

🗣 Speaker Laura Summers Laura is a very technical designer™️, working at Pydantic as Lead Design Engineer. Her side projects include Sweet Summer Child Score (summerchild.dev) and Ethics Litmus Tests (ethical-litmus.site). Laura is passionate about feminism, digital rights and designing for privacy. She speaks, writes and runs workshops at the intersection of design and technology.

👋 About Us Stay in touch with the community! Join our Slack: https://bit.ly/EmpoweredInTechSlack We are a local community in Berlin dedicated to empowering FLINTA (women, lesbians, intersex, non-binary, trans and agender) people to excel in their tech journey. Our events offer study groups, technical workshops, hackathons, networking events, panel discussions, lightning talks, and social events.

☂️ About the FLINTA label This event is labeled as FLINTA (women, lesbians, intersex, non-binary, trans and agender) as we make it a mission to empower women and other underrepresented minorities in the tech community. That being said, we don't exclude anybody! Cis men are welcome to join 😊, just bear in mind that the topics will be discussed from this perspective.

💗 Code of Conduct We are dedicated to providing a safe and welcoming experience for everyone who participates in our events. Our events aim to empower diverse women and we welcome everyone who identifies as a woman or another underrepresented group in tech or an ally. We follow the Berlin Code of Conduct for our events: https://berlincodeofconduct.org/

📸 Media Consent We may be taking photos of this event for social media posts. If you do not want to be photographed, please let the organizers at the event know. We will make sure to respect your privacy.

PLEASE NOTE: Clicking 'attend' does not register you for the event. You will need to register for the event on the link provided below to receive a QR code ticket via email. If you do not have a QR code, you will not be able to attend the event.

Sign up here!

Join us in London for our next in-person meet-up in partnership with The Trade Desk on Wednesday 19th March 2025. A free evening of tech talks, a chance to network with the community with refreshments and food provided.

📆 Date: Wednesday 19th March 2025 ⏰ Time: 6:00 pm - 9:00 pm GMT 🗣️ Title: From Theory to Scale: Building Machine Learning Solutions for Complex Optimisation 📍 Location: The Trade Desk UK – 10th Floor, Barts Square, One Bartholomew Cl, London EC1A 7BL

How do you take machine learning from theory to real-world impact? Join us for an engaging technical talk and panel discussion exploring the challenges of building scalable ML solutions to optimise complex systems. Experts from The Trade Desk will share insights into designing and deploying models that drive efficiency in large-scale decision-making environments.

We’ll discuss practical approaches to improving supply path optimisation, balancing trade-offs between cost and performance, and scaling models to handle vast amounts of real-time data. This session will provide valuable strategies and best practices for applying ML to real-world challenges.

Following the technical talk, a panel of data scientists from The Trade Desk will share their journeys from diverse academic backgrounds to thriving careers in data science. Gain valuable insights on transitioning from academia to industry and discover the skills and experiences that can pave the way for a successful career in data science.

The full speaker lineup will be confirmed soon!

Agenda: 6:00 PM – Doors open – drinks, food and networking 6:45 PM – Intro from DSF and TTD 6:50 PM – Talk session 7:30 PM – Comfort break 7:40 PM – Panel discussion 8:30 PM – Drinks and networking 9:00 PM – Close

Food and drinks are included.

Sign up for this event here!

PLEASE NOTE: Clicking 'attend' does not register you for the event. You will need to register for the event on the link provided below to receive a QR code ticket via email. If you do not have a QR code, you will not be able to attend the event.