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:
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
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
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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
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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/
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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
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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
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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
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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/
In this episode, We talked with Pastor, a medical doctor who built a career in machine learning while studying medicine. Pastor shares how he balanced both fields, leveraged live courses and public sharing to grow his skills, and found opportunities through freelancing and mentoring.TIMECODES00:00 Pastor’s background and early programming journey06:05 Learning new tools and skills on the job while studying medicine11:44 Balancing medical studies with data science work and motivation13:48 Applying medical knowledge to data science and vice versa18:44 Starting freelance work on Upwork and overcoming language challenges24:03 Joining the machine learning engineering course and benefits of live cohorts27:41 Engaging with the course community and sharing progress publicly35:16 Using LinkedIn and social media for career growth and interview opportunities41:03 Building reputation, structuring learning, and leveraging course projects50:53 Volunteering and mentoring with DeepLearning.AI and Stanford Coding Place57:00 Managing time and staying productive while studying medicine and machine learningConnect with Pastor
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Struggling with data trust issues, dashboard drama, or constant pipeline firefighting? In this deep‑dive interview, Lior Barak shows you how to shift from a reactive “fix‑it” culture to a mindful, impact‑driven practice rooted in Zen/Wabi‑Sabi principles.
You’ll learn:
Why 97 % of CEOs say they use data, but only 24 % call themselves data‑driven
The traffic‑light dashboard pattern (green / yellow / red) that instantly tells execs whether numbers are safe to use
A practical rule for balancing maintenance, rollout, and innovation—and avoiding team burnout
How to quantify ROI on data products, kill failing legacy systems, and handle ad‑hoc exec requests without derailing roadmaps
Turning “imperfect” data into business value with mindful communication, root‑cause logs, and automated incident review loops
🕒 TIMECODES
00:00 Community and mindful data strategy
04:06 Career journey and product management insights
08:03 Wabi-sabi data and the trust crisis
11:47 AI, data imperfection, and trust challenges
20:05 Trust crisis examples and root cause analysis
25:06 Regaining trust through mindful data management
30:47 Traffic light system and effective communication
37:41 Communication gaps and team workload balance
39:58 Maintenance stress and embracing Zen mindset
49:29 Accepting imperfection and measuring impact
56:19 Legacy systems and managing executive requests
01:00:23 Role guidance and closing reflections
🔗 Connect with Lior
LinkedIn - https://www.linkedin.com/in/liorbarak
Website - https://cookingdata.substack.com/
Cooking Data newsletter: https://cookingdata.substack.com/
Product product lifecycle manager: https://app--data-product-lifecycle-manager-c81b10bb.base44.app/
🔗 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/u/0/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://x.com/DataTalksClub
Website - https://datatalks.club/
🔗 Connect with Alexey
Twitter - https://x.com/Al_Grigor
Linkedin - https://www.linkedin.com/in/agrigorev/
In this episode, we talk with Orell about his journey from electrical engineering to freelancing in data engineering. Exploring lessons from startup life, working with messy industrial data, the realities of freelancing, and how to stay up to date with new tools.
Topics covered:
A practical conversation for listeners who are curious about moving from research or permanent roles into freelance data engineering.
🕒 TIMECODES
0:00 Orel’s career and move to freelancing
9:04 Startup experience and data engineering lessons
16:05 Academia vs. startups and starting freelancing
25:33 Early freelancing challenges and networking
34:22 Freelance data engineering and messy industrial data
43:27 Staying practical, learning tools, and growth
50:33 Freelancing challenges and client acquisition
58:37 Tools, problem-solving, and manual work
🔗 CONNECT WITH ORELL
Twitter - https://bsky.app/profile/orgarten.bsk...
LinkedIn - / ogarten
Github - https://github.com/orgarten
Website - https://orellgarten.com
🔗 CONNECT WITH DataTalksClub
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/
🔗 CONNECT WITH ALEXEY
Connect with Alexey
Twitter - / al_grigor
Linkedin - / agrigorev
Thinking about swapping your 9‑to‑5 for client work, but worried that a long German–style notice period will kill your chances? In this live interview, seven‑year data‑freelance veteran Dimitri walks through his experience of taking his freelance career to the next level.
About the Speaker:
Dimitri Visnadi is an independent data consultant with a focus on data strategy. He has been consulting companies leading the marketing data space such as Unilever, Ferrero, Heineken, and Red Bull.
He has lived and worked in 6 countries across Europe in both corporate and startup organizations. He was part of data departments at Hewlett-Packard (HP) and a Google partnered consulting firm where he was working on data products and strategy.
Having received a Masters in Business Analytics with Computer Science from University College London and a Bachelor in Business Administration from John Cabot University, Dimitri still has close ties to academia and holds a mentor position in entrepreneurship at both institutions.
🕒 TIMECODES00:00 Dimitri’s journey from corporate to freelance data specialist05:41 Job tenure trends, tech career shifts, and freelance types10:50 Freelancing challenges, success, and finding clients17:33 Freelance market trends and Dimitri’s job board23:51 Starting points, top freelance skills, and market insights32:48 Building a lifestyle business: scaling and work-life balance45:30 Data Freelancer course and marketing for freelancers48:33 Subscription services and managing client relationships56:47 Pricing models and transitioning advice1:01:02 Notice periods, networking, and risks in freelancing transition
🔗 CONNECT WITH DataTalksClub
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
LinkedIn - / datatalks-club
Twitter - / datatalksclub
Website - https://datatalks.club/
🔗 CONNECT WITH DIMITRI
Linkedin - https://www.linkedin.com/in/visnadi/
In this podcast episode, we talked with Will Russell about From Hackathons to Developer Advocacy.About the Speaker:
Will Russell is a Developer Advocate at Kestra, known for his videos on workflow orchestration. Previously, Will built open source education programs to help up and coming developers make their first contributions in open source. With a passion for developer education, Will creates technical video content and documentation that makes technologies more approachable for developers.In this episode, we sit down with Will—developer advocate, content creator, and passionate community builder. We’ll hear about his unique path through tech, the lessons he’s learned, and his approach to making complex topics accessible and engaging. Whether you’re curious about open source, hackathons, or what it’s like to bridge the gap between developers and the broader tech community, this conversation is full of insights and inspiration.🕒 TIMECODES
0:00 Introduction, career journeys, and video setup and workflow
10:41 From hackathons to open source: Early experiences and learning
16:04 Becoming a hackathon organizer and the value of soft skills
23:18 How to organize a hackathon, memorable projects, and creativity
33:39 Major League Hacking: Building community and scaling student programs
41:16 Mentorship, development environments, and onboarding in open source
49:14 Developer advocacy, content strategy, and video tips
57:16 Will’s current projects and future plans for content creation
🔗 CONNECT WITH DataTalksClubJoin the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/
🔗 CONNECT WITH WILLLinkedIn - https://www.linkedin.com/in/wrussell1999/
Twitter - https://x.com/wrussell1999
GitHub - https://github.com/wrussell1999
Website - https://wrussell.co.uk/
In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data.
About the Speaker:
Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024.
In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.
In this episode, we talk about Lavanya Gupta’s journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.
🕒 TIMECODES
00:00 Lavanya’s journey from software engineer to AI researcher
10:15 Benchmarking long context language models
12:36 Limitations of large context models in real domains
14:54 Handling large documents and publishing research in industry
19:45 Building a data science career: publications, motivation, and mentorship
25:01 Self-learning, hackathons, and networking
33:24 Community work and Kaggle projects
37:32 Mentorship and open-ended guidance
51:28 Building a strong data science portfolio
🔗 CONNECT WITH LAVANYALinkedIn - / lgupta18 🔗 CONNECT WITH DataTalksClub 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 LinkedIn - / datatalks-club Twitter - / datatalksclub Website - https://datatalks.club/
In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps
About the Speaker:
Eddy Zulkifly is a Staff Data Engineer at Kinaxis, building robust data platforms across Google Cloud, Azure, and AWS. With a decade of experience in data, he actively shares his expertise as a Mentor on ADPList and Teaching Assistant at Uplimit. Previously, he was a Senior Data Engineer at Home Depot, specializing in e-commerce and supply chain analytics. Currently pursuing a Master’s in Analytics at the Georgia Institute of Technology, Eddy is also passionate about open-source data projects and enjoys watching/exploring the analytics behind the Fantasy Premier League.
In this episode, we dive into the world of data engineering and FinOps with Eddy Zulkifly, Staff Data Engineer at Kinaxis. Eddy shares his unconventional career journey—from optimizing physical warehouses with Excel to building digital data platforms in the cloud.
🕒 TIMECODES
0:00 Eddy’s career journey: From supply chain to data engineering
8:18 Tools & learning: Excel, Docker, and transitioning to data engineering
21:57 Physical vs. digital warehousing: Analogies and key differences
31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations
40:18 Resources for FinOps: Certifications and the FinOps Foundation
45:12 Standardizing cloud cost reporting across AWS/GCP/Azure
50:04 Eddy’s master’s degree and closing thoughts
🔗 CONNECT WITH EDDY
Twitter - https://x.com/eddarief
Linkedin - https://www.linkedin.com/in/eddyzulkifly/
Github: https://github.com/eyzyly/eyzyly
ADPList: https://adplist.org/mentors/eddy-zulkifly
🔗 CONNECT WITH DataTalksClub
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
LinkedIn - https://www.linkedin.com/company/datatalks-club/
Twitter - https://twitter.com/DataTalksClub
Website - https://datatalks.club/
In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.
About the Speaker:
Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days.
In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.
0:00 Introduction to Bartosz and his background
4:00 Bartosz’s career journey from Java development to AI engineering
9:05 The importance of testing in data engineering
11:19 How to create tests for data pipelines
13:14 Tools and approaches for testing data pipelines
17:10 Choosing Spark for data engineering projects
19:05 The connection between data engineering and AI tools
21:39 Use cases of AI in data engineering and MLOps
25:13 Prompt engineering techniques and best practices
31:45 Prompt compression and caching in AI models
33:35 Thoughts on DeepSeek and open-source AI models
35:54 Using AI for lead classification and LinkedIn automation
41:04 Building Chrome extensions with AI integration
43:51 Comparing Cursor and GitHub Copilot for coding
47:11 Using ChatGPT and Perplexity for AI-assisted tasks
52:09 Hosting static websites and using AI for development
54:27 How blogging helps attract clients and share knowledge
58:15 Using AI to assist with writing and content creation
🔗 CONNECT WITH Bartosz
LinkedIn: https://www.linkedin.com/in/mikulskibartosz/
Github: https://github.com/mikulskibartosz
Website: https://mikulskibartosz.name/blog/
🔗 CONNECT WITH DataTalksClub
Join the community - https://datatalks.club/slack.html
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In this podcast episode, we talked with Nemanja Radojkovic about MLOps in Corporations and Startups.
About the Speaker:
Nemanja Radojkovic is Senior Machine Learning Engineer at Euroclear.
In this event,we’re diving into the world of MLOps, comparing life in startups versus big corporations. Joining us again is Nemanja, a seasoned machine learning engineer with experience spanning Fortune 500 companies and agile startups. We explore the challenges of scaling MLOps on a shoestring budget, the trade-offs between corporate stability and startup agility, and practical advice for engineers deciding between these two career paths. Whether you’re navigating legacy frameworks or experimenting with cutting-edge tools.
1:00 MLOps in corporations versus startups
6:03 The agility and pace of startups
7:54 MLOps on a shoestring budget
12:54 Cloud solutions for startups
15:06 Challenges of cloud complexity versus on-premise
19:19 Selecting tools and avoiding vendor lock-in
22:22 Choosing between a startup and a corporation
27:30 Flexibility and risks in startups
29:37 Bureaucracy and processes in corporations
33:17 The role of frameworks in corporations
34:32 Advantages of large teams in corporations
40:01 Challenges of technical debt in startups
43:12 Career advice for junior data scientists
44:10 Tools and frameworks for MLOps projects
49:00 Balancing new and old technologies in skill development
55:43 Data engineering challenges and reliability in LLMs
57:09 On-premise vs. cloud solutions in data-sensitive industries
59:29 Alternatives like Dask for distributed systems
🔗 CONNECT WITH NEMANJA
LinkedIn - / radojkovic
Github - https://github.com/baskervilski
🔗 CONNECT WITH DataTalksClub
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
LinkedIn - / datatalks-club
Twitter - / datatalksclub
Website - https://datatalks.club/
In this podcast episode, we talked with Adrian Brudaru about the past, present and future of data engineering.
About the speaker:
Adrian Brudaru studied economics in Romania but soon got bored with how creative the industry was, and chose to go instead for the more factual side. He ended up in Berlin at the age of 25 and started a role as a business analyst. At the age of 30, he had enough of startups and decided to join a corporation, but quickly found out that it did not provide the challenge he wanted.
As going back to startups was not a desirable option either, he decided to postpone his decision by taking freelance work and has never looked back since. Five years later, he co-founded a company in the data space to try new things. This company is also looking to release open source tools to help democratize data engineering.
0:00 Introduction to DataTalks.Club
1:05 Discussing trends in data engineering with Adrian
2:03 Adrian's background and journey into data engineering
5:04 Growth and updates on Adrian's company, DLT Hub
9:05 Challenges and specialization in data engineering today
13:00 Opportunities for data engineers entering the field
15:00 The "Modern Data Stack" and its evolution
17:25 Emerging trends: AI integration and Iceberg technology
27:40 DuckDB and the emergence of portable, cost-effective data stacks
32:14 The rise and impact of dbt in data engineering
34:08 Alternatives to dbt: SQLMesh and others
35:25 Workflow orchestration tools: Airflow, Dagster, Prefect, and GitHub Actions
37:20 Audience questions: Career focus in data roles and AI engineering overlaps
The role of semantics in data and AI workflows
41:11 Focusing on learning concepts over tools when entering the field
45:15 Transitioning from backend to data engineering: challenges and opportunities
47:48 Current state of the data engineering job market in Europe and beyond
49:05 Introduction to Apache Iceberg, Delta, and Hudi file formats
50:40 Suitability of these formats for batch and streaming workloads
52:29 Tools for streaming: Kafka, SQS, and related trends
58:07 Building AI agents and enabling intelligent data applications
59:09Closing discussion on the place of tools like DBT in the ecosystem
🔗 CONNECT WITH ADRIAN BRUDARU
Linkedin - / data-team Website - https://adrian.brudaru.com/ 🔗 CONNECT WITH DataTalksClub
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 LinkedIn - /datatalks-club Twitter - /datatalksclub Website - https://datatalks.club/