DataTalks.Club

• How to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-AndersonHow to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-Anderson

  In this talk, Hugo Bowne-Anderson, an independent data and AI consultant, educator, and host of the podcasts Vanishing Gradients and High Signal, shares his journey from academic research and curriculum design at DataCamp to advising teams at Netflix, Meta, and the US Air Force. Together, we explore how to build reliable, production-ready AI systems—from prompt evaluation and dataset design to embedding agents into everyday workflows.You’ll learn about:- How to structure teams and incentives for successful AI adoption- Practical prompting techniques for accurate timestamp and data generation- Building and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoring- Tools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)- The evolution of AI agents—from simple RAG systems to proactive, embedded assistants- How to escape “proof of concept purgatory” and prioritize AI projects that drive business value- Step-by-step guidance for building reliable, evaluable AI agentsThis session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you’re optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.LINKS- Escaping POC Purgatory: Evaluation-Driven Development for AI Systems - https://www.oreilly.com/radar/escaping-poc-purgatory-evaluation-driven-development-for-ai-systems/- Stop Building AI Agents - https://www.decodingai.com/p/stop-building-ai-agents- How to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.be- My Vanishing Gradients Substack - https://hugobowne.substack.com/- Building LLM Applications for Data Scientists and Software Engineers - https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclubTIMECODES:00:00 Introduction and Expertise04:04 Transition to Freelance Consulting and Advising08:49 Restructuring Teams and Incentivizing AI Adoption12:22 Improving Prompting for Timestamp Generation17:38 Evaluation Sets and Failure Analysis for Reliable Software23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets27:38 Software Tools for Evaluation and Monitoring33:14 Evolution of AI Tools: Proactivity and Embedded Agents40:12 The Future of AI is Not Just Chat44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value50:19 RAG vs. Agents: Complexity and Power Trade-Offs56:21 Recommended Steps for Building Agents59:57 Defining Memory in Multi-Turn ConversationsConnect with Hugo- Twitter - https://x.com/hugobowne- Linkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/- Github - https://github.com/hugobowne- Website - https://hugobowne.github.io/Connect 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/

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• From Biotechnology to Bioinformatics Software - Sebastian Ayala RuanoFrom Biotechnology to Bioinformatics Software - Sebastian Ayala Ruano

  In this talk, Sebastian, a bioinformatics researcher and software engineer, shares his inspiring journey from wet lab biotechnology to computational bioinformatics. Hosted by Data Talks Club, this session explores how data science, AI, and open-source tools are transforming modern biological research — from DNA sequencing to metagenomics and protein structure prediction.You’ll learn about: - The difference between wet lab and dry lab workflows in biotechnology - How bioinformatics enables faster insights through data-driven modeling - The MCW2 Graph Project and its role in studying wastewater microbiomes - Using co-abundance networks and the CC Lasso algorithm to map microbial interactions - How AlphaFold revolutionized protein structure prediction - Building scientific knowledge graphs to integrate biological metadata - Open-source tools like VueGen and VueCore for automating reports and visualizations - The growing impact of AI and large language models (LLMs) in research and documentation - Key differences between R (BioConductor) and Python ecosystems for bioinformaticsThis talk is ideal for data scientists, bioinformaticians, biotech researchers, and AI enthusiasts who want to understand how data science, AI, and biology intersect. Whether you work in genomics, computational biology, or scientific software, you’ll gain insights into real-world tools and workflows shaping the future of bioinformatics.Links:- MicW2Graph: https://zenodo.org/records/12507444- VueGen: https://github.com/Multiomics-Analytics-Group/vuegen- Awesome-Bioinformatics: https://github.com/danielecook/Awesome-BioinformaticsTIMECODES00:00 Welcome to Data Talks Club03:15 Sebastian’s Journey into Bioinformatics06:20 From Wet Lab to Computational Biology10:05 Wet Lab vs Dry Lab Explained13:25 Bioinformatics as Data Science for Biology16:25 How DNA Sequencing Works19:55 MCW2 Graph and Wastewater Microbiomes23:05 Building Microbial Networks with CC Lasso27:00 Protein–Ligand Simulation Basics30:08 Predicting Protein Folding in 3D33:43 AlphaFold Revolution in Protein Prediction36:55 Inside the MCW2 Knowledge Graph40:35 VueGen: Automating Scientific Reports44:00 VueCore: Visualizing OMIX Data47:18 Using AI and LLMs in Bioinformatics51:08 R vs Python in Bioinformatics Tools54:13 Closing Thoughts from EcuadorConnect with Sebastian- Twitter - https://twitter.com/sayalaruano- Linkedin - https://linkedin.com/in/sayalaruano - Github - https://github.com/sayalaruano- Website - https://sayalaruano.github.io/Connect 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/

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• Lessons from Applied AI: Tesla, Waymo, and Beyond - Aishwarya Jadhav

  In this episode, we talked with Aishwarya Jadhav, a machine learning engineer whose career has spanned Morgan Stanley, Tesla, and now Waymo. Aishwarya shares her journey from big data in finance to applied AI in self-driving, gesture understanding, and computer vision. She discusses building an AI guide dog for the visually impaired, contributing to malaria mapping in Africa, and the challenges of deploying safe autonomous systems. We also explore the intersection of computer vision, NLP, and LLMs, and what it takes to break into the self-driving AI industry.TIMECODES00:51 Aishwarya’s career journey from finance to self-driving AI05:45 Building AI guide dog for the visually impaired12:03 Exploring LiDAR, radar, and Tesla’s camera-based approach16:24 Trust, regulation, and challenges in self-driving adoption19:39 Waymo, ride-hailing, and gesture recognition for traffic control24:18 Malaria mapping in Africa and AI for social good29:40 Deployment, safety, and testing in self-driving systems37:00 Transition from NLP to computer vision and deep learning43:37 Reinforcement learning, robotics, and self-driving constraints51:28 Testing processes, evaluations, and staged rollouts for autonomous driving52:53 Can multimodal LLMs be applied to self-driving?55:33 How to get started in self-driving AI careersConnect with Aishwarya- Linkedin - https://www.linkedin.com/in/aishwaryajadhav8/Connect 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/

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• Building reliable AI products in the era of Gen AI and Agents - Ranjitha Kulkarni

  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/

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• From Theme Parks to Tesla: Building Data Products That Work

  In this episode, we talked with Abouzar Abbaspour, a data engineer whose career spans software engineering in Iran, building crowd and recommendation systems at a Dutch theme park, deploying large-scale ML models at Bol.com, and now working at Tesla. Abouzar shares how he bridged diverse industries, tackled real-world data challenges, and adapted to new roles while keeping a hands-on approach to machine learning and engineering.TIMECODES00:00 Career journey and early motivations06:17 Moving to Europe for data science12:18 Working with theme parks and crowd modeling18:29 Lessons from ride and visitor data23:06 Building recommendation systems at Efteling27:26 Joining Bol.com and the Dutch e-commerce industry32:49 Product and brand recommendation logic36:09 Experimenting with "Tinder for brands"40:26 Engagement metrics and product validation43:02 From ML engineering to data engineering roles52:04 Hands-on skills at Tesla and industry expectations57:43 Career growth, learning, and adviceConnect with AbouzarLinkedin -   / abouzar-abbaspour  Website - https://www.abouzar-abbaspour.com/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

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