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 agents

This 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=datatalksclub

TIMECODES:

00:00 Introduction and Expertise

04:04 Transition to Freelance Consulting and Advising

08:49 Restructuring Teams and Incentivizing AI Adoption

12:22 Improving Prompting for Timestamp Generation

17:38 Evaluation Sets and Failure Analysis for Reliable Software

23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets

27:38 Software Tools for Evaluation and Monitoring

33:14 Evolution of AI Tools: Proactivity and Embedded Agents

40:12 The Future of AI is Not Just Chat

44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value

50:19 RAG vs. Agents: Complexity and Power Trade-Offs

56:21 Recommended Steps for Building Agents

59:57 Defining Memory in Multi-Turn Conversations

Connect 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/

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 bioinformatics

This 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-Bioinformatics

TIMECODES00:00 Sebastian’s Journey into Bioinformatics06:02 From Wet Lab to Computational Biology08:23 Wet Lab vs Dry Lab Explained12:35 Bioinformatics as Data Science for Biology15:30 How DNA Sequencing Works19:29 MCW2 Graph and Wastewater Microbiomes23:10 Building Microbial Networks with CC Lasso26:54 Protein–Ligand Simulation Basics29:58 Predicting Protein Folding in 3D33:30 AlphaFold Revolution in Protein Prediction36:45 Inside the MCW2 Knowledge Graph39:54 VueGen: Automating Scientific Reports43:56 VueCore: Visualizing OMIX Data47:50 Using AI and LLMs in Bioinformatics50:25 R vs Python in Bioinformatics Tools53:17 Closing Thoughts from Ecuador

Connect 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/

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/

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/

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.html
• Subscribe 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-events
• GitHub: https://github.com/DataTalksClub
• LinkedIn -   / datatalks-club  
• Twitter -   / datatalksclub  
• Website - https://datatalks.club/

In this episode, we chat with Dashel Ruiz, whose journey spans semiconductors, machine learning, and teaching. Dashel shares how he transitioned from hardware to data science, navigated complex projects in diverse industries, and now combines technical expertise with a passion for teaching. Tune in to hear insights on building a career in data, mastering new technologies, and making an impact both in the lab and the classroom.

TIMECODES

00:00 Dashel's unique career path from music to semiconductors

06:16 The transition into data and software engineering at Microchip

11:44 Discovering machine learning to solve real problems in semiconductor manufacturing

20:40 How Dashel found and his experience with the Machine Learning Zoomcamp

29:33 The practical advantages of DataTalks.Club courses over other platforms

39:52 Overcoming challenges and the value of the learning community

48:10 Hands-on project experience: From image classification to Kaggle competitions

54:12 Staying motivated throughout the long-term course

59:55 The importance of deployment and full-stack ML skills

1:07:36 Closing thoughts on teaching and future courses

Connect with Dashel

• Linkedin - https://www.linkedin.com/in/dashel-ruiz-perez-2b036172/

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/

In this episode, we talk with Michael Lanham, an AI and software innovator with over two decades of experience spanning game development, fintech, oil and gas, and agricultural tech. Michael shares his journey from building neural network-based games and evolutionary algorithms to writing influential books on AI agents and deep learning. He offers insights into the evolving AI landscape, practical uses of AI agents, and the future of generative AI in gaming and beyond.TIMECODES00:00 Micheal Lanham’s career journey and AI agent books05:45 Publishing journey: AR, Pokémon Go, sound design, and reinforcement learning10:00 Evolution of AI: evolutionary algorithms, deep learning, and agents13:33 Evolutionary algorithms in prompt engineering and LLMs18:13 AI agent books second edition and practical applications20:57 AI agent workflows: minimalism, task breakdown, and collaboration26:25 Collaboration and orchestration among AI agents31:24 Tools and reasoning servers for agent communication35:17 AI agents in game development and generative AI impact38:57 Future of generative AI in gaming and immersive content41:42 Coding agents, new LLMs, and local deployment45:40 AI model trends and data scientist career advice53:36 Cognitive testing, evaluation, and monitoring in AI58:50 Publishing details and closing remarksConnect with Micheal

• Linkedin - https://www.linkedin.com/in/micheal-lanham-189693123/

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/...
• Check other upcoming events - https://lu.ma/dtc-events
• GitHub: https://github.com/DataTalksClub
• LinkedIn -   / datatalks-club  
• Twitter -   / datatalksclub  
• Website - https://datatalks.club/

At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.

- Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.

- Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.

- Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.

- Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.

- Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.

Igor Kvachenok

Master’s student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.

Connect: https://www.linkedin.com/in/igor-kvachenok/

Selim Nowicki

Founder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today’s ML tooling and dbt’s impact on analytics.

Connect: https://www.linkedin.com/in/selim-nowicki/

Gülsah Durmaz

Architect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.

Connect: https://www.linkedin.com/in/gulsah-durmaz/

Yashasvi (Yashi) Misra

Data Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.

Connect: https://www.linkedin.com/in/misrayashasvi/

Mehdi Ouazza

Developer Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.

Connect: https://www.linkedin.com/in/mehd-io/

In this episode, we talk with Daniel, an astrophysicist turned machine learning engineer and AI ambassador. Daniel shares his journey bridging astronomy and data science, how he leveraged live courses and public knowledge sharing to grow his skills, and his experiences working on cutting-edge radio astronomy projects and AI deployments. He also discusses practical advice for beginners in data and astronomy, and insights on career growth through community and continuous learning.TIMECODES00:00 Lunar eclipse story and Daniel’s astronomy career04:12 Electromagnetic spectrum and MEERKAT data explained10:39 Data analysis and positional cross-correlation challenges15:25 Physics behind radio star detection and observation limits16:35 Radio astronomy’s advantage and machine learning potential20:37 Radio astronomy progress and Daniel’s ML journey26:00 Python tools and experience with ZoomCamps31:26 Intel internship and exploring LLMs41:04 Sharing progress and course projects with orchestration tools44:49 Setting up Airflow 3.0 and building data pipelines47:39 AI startups, training resources, and NVIDIA courses50:20 Student access to education, NVIDIA experience, and beginner astronomy programs57:59 Skills, projects, and career advice for beginners59:19 Starting with data science or engineering1:00:07 Course sponsorship, data tools, and learning resourcesConnect with Daniel

• Linkedin -   / egbodaniel  

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/...
• Check other upcoming events - https://lu.ma/dtc-events
• GitHub: https://github.com/DataTalksClub
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• Website - https://datatalks.club/

At Berlin Buzzwords, industry voices highlighted how search is evolving with AI and LLMs.

- Kacper Łukawski (Qdrant) stressed hybrid search (semantic + keyword) as core for RAG systems and promoted efficient embedding models for smaller-scale use.

- Manish Gill (ClickHouse) discussed auto-scaling OLAP databases on Kubernetes, combining infrastructure and database knowledge.

- André Charton (Kleinanzeigen) reflected on scaling search for millions of classifieds, moving from Solr/Elasticsearch toward vector search, while returning to a hands-on technical role.

- Filip Makraduli (Superlinked) introduced a vector-first framework that fuses multiple encoders into one representation for nuanced e-commerce and recommendation search.

- Brian Goldin (Voyager Search) emphasized spatial context in retrieval, combining geospatial data with AI enrichment to add the “where” to search.

- Atita Arora (Voyager Search) highlighted geospatial AI models, the renewed importance of retrieval in RAG, and the cautious but promising rise of AI agents.

Together, their perspectives show a common thread: search is regaining center stage in AI—scaling, hybridization, multimodality, and domain-specific enrichment are shaping the next generation of retrieval systems.

Kacper Łukawski

Senior Developer Advocate at Qdrant, he educates users on vector and hybrid search. He highlighted Qdrant’s support for dense and sparse vectors, the role of search with LLMs, and his interest in cost-effective models like static embeddings for smaller companies and edge apps.

Connect: https://www.linkedin.com/in/kacperlukawski/

Manish Gill

Engineering Manager at ClickHouse, he spoke about running ClickHouse on Kubernetes, tackling auto-scaling and stateful sets. His team focuses on making ClickHouse scale automatically in the cloud. He credited its speed to careful engineering and reflected on the shift from IC to manager.

Connect: https://www.linkedin.com/in/manishgill/

André Charton

Head of Search at Kleinanzeigen, he discussed shaping the company’s search tech—moving from Solr to Elasticsearch and now vector search with Vespa. Kleinanzeigen handles 60M items, 1M new listings daily, and 50k requests/sec. André explained his career shift back to hands-on engineering.

Connect: https://www.linkedin.com/in/andrecharton/

Filip Makraduli

Founding ML DevRel engineer at Superlinked, an open-source framework for AI search and recommendations. Its vector-first approach fuses multiple encoders (text, images, structured fields) into composite vectors for single-shot retrieval. His Berlin Buzzwords demo showed e-commerce search with natural-language queries and filters.

Connect: https://www.linkedin.com/in/filipmakraduli/

Brian Goldin

Founder and CEO of Voyager Search, which began with geospatial search and expanded into documents and metadata enrichment. Voyager indexes spatial data and enriches pipelines with NLP, OCR, and AI models to detect entities like oil spills or windmills. He stressed adding spatial context (“the where”) as critical for search and highlighted Voyager’s 12 years of enterprise experience.

Connect: https://www.linkedin.com/in/brian-goldin-04170a1/

Atita Arora

Director of AI at Voyager Search, with nearly 20 years in retrieval systems, now focused on geospatial AI for Earth observation data. At Berlin Buzzwords she hosted sessions, attended talks on Lucene, GPUs, and Solr, and emphasized retrieval quality in RAG systems. She is cautiously optimistic about AI agents and values the event as both learning hub and professional reunion.

Connect: https://www.linkedin.com/in/atitaarora/