DataTalks.Club

In this talk, Aditya, an experienced AI Researcher and Engineer, shares his technical evolution—from his roots in embedded systems to building complex, large-scale AI agent architectures. We explore the practical challenges of enterprise AI adoption, the shifting economics of LLMs, and the infrastructure required to deploy reliable multi-agent systems.You’ll learn about:- The ROI of Fine-Tuning: How to decide between specialized small models and general-purpose APIs based on cost and latency.- Agent MLOps Stack: The essential roles of guardrails, data lineage, and auditability in AI workflows.- Reliability in High-Stakes Verticals: Navigating the unique AI deployment challenges in the legal and healthcare sectors.- Evaluation Frameworks: How to design robust evals for multi-tenancy systems at scale.- Human-in-the-Loop: Strategies for aligning "LLM as a judge" with human-labeled ground truth to eliminate bias.- The Future of AGI: What to expect from the next wave of multimodal agents and autonomous systems.TIMECODES: 00:00 Aditya’s from embedded systems to AI08:52 Enterprise AI research and adoption gaps 13:13 AI reliability in legal and healthcare 19:16 Specialized models and agent governance 24:58 LLM economics: Fine-tuning vs. API ROI 30:26 Agent MLOps: Guardrails and data lineage 36:55 Iterating on agents with user feedback 43:30 AI evals for multi-tenancy and scale 50:18 Aligning LLM judges with human labels 56:40 Agent infrastructure and deployment risks 1:02:35 Future of AGI and multimodal agentsThis talk is designed for Machine Learning Engineers, Data Scientists, and Technical Product Managers who are moving beyond AI prototypes and into production-grade agentic workflows. It is especially relevant for those working in regulated industries or managing high-volume API budgets.Connect with Aditya:- Linkedin - https://www.linkedin.com/in/aditya-gautam-68233a30/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, Juan, Analytics Engineer and author of Fundamentals of Analytics Engineering share his professional journey from studying psychological research in Colombia to becoming one of the first analytics engineers in the Netherlands. We explore the evolution of the role, the shift toward engineering rigor in data modeling, and how the landscape of tools like dbt and Databricks is changing the way teams work.

You’ll learn about:

• The fundamental differences between traditional BI engineering and modern analytics engineering.
• How to bridge the gap between business stakeholders and technical data infrastructure.
• The technical "glue" that connects Python and SQL for robust data pipelines.
• The importance of automated testing (generic vs. singular tests) to prevent "silent" data failures.
• Strategies for modeling messy, fragmented source data into a unified "business reality."
• The current state of the "Lakehouse" paradigm and how it impacts storage and compute costs.
• Expert advice on navigating the dbt ecosystem and its emerging competitors.

Links:

• DE Course: https://github.com/DataTalksClub/data-engineering-zoomcamp
• Luma: https://luma.com/0uf7mmup

TIMECODES:

0:00 Juan’s psychological research and transition to data

4:36 Riding the wave: The early days of analytics engineering

7:56 Breaking down the gap between analysts and engineers

11:03 The art of turning business reality into clean data

16:25 Why data engineering is about safety, not just speed

20:53 Reimagining data modeling in the modern era

26:53 To split or not to split: Finding the right team roles

30:35 Python, SQL, and the technical toolkit for success

38:41 How to stop manually testing your data dashboards

46:34 Bringing software engineering rigor to data workflows

49:50 Must-read books and resources for mastering the craft

55:42 The future of dbt and the shifting tool landscape

1:00:29 Deciphering the lakehouse: Warehousing in the cloud

1:11:16 Pro-tips for starting your data engineering journey

1:14:40 The big debate: Databricks vs. Snowflake

1:18:28 Why every data professional needs a local community

This talk is designed for data analysts looking to level up their engineering skills, data engineers interested in the business-logic layer, and data leaders trying to structure their teams more effectively. It is particularly valuable for those preparing for the Data Engineering Zoomcamp or anyone looking to transition into an Analytics Engineering role.

Connect with Juan

• Linkedin - https://www.linkedin.com/in/jmperafan/
• Website - https://juanalytics.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/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 of DataTalks.Club, Paul Iusztin, founding AI engineer and author of the LLM Engineer’s Handbook, breaks down the transition from traditional software development to production-grade AI engineering.

We explore the essential skill stack for 2026, the shift from "PoC purgatory" to shipping real products, and why the future of the field belongs to the full-stack generalist.

You’ll learn about:

- Why the role is evolving into the "new software engineer" and how to own the full product lifecycle.

- Identifying when to use traditional ML (like XGBoost) over LLMs to avoid over-engineering.

- The architectural shift from fine-tuning to mastering data pipelines and semantic search.

- Reliable Agentic Workflows- How to use coding assistants like Claude and Cursor to act as an architect rather than just a coder.

- Why human-in-the-loop evaluation is the most critical bottleneck in shipping reliable AI.

- How to build a "Second Brain" portfolio project that proves your end-to-end engineering value.

Links:

- Course link: https: https://academy.towardsai.net/courses/agent-engineering?ref=b3ab31

- Decoding AI Magazine: https://www.decodingai.com/

TIMECODES:

00:00 From code to cars: Paul’s journey to AI

07:08 Deep learning and the autonomous driving challenge

12:09 The transition to global product engineering

15:13 Survival guide: Data science vs. AI engineering

22:29 The full-stack AI engineer skill stack

29:12 Mastering RAG and knowledge management

32:27 The generalist edge: Learning with AI

42:21 Technical pillars for shipping AI products

54:05 Portfolio secrets and the "second brain"

58:01 The future of the LLM engineer’s handbook

This talk is designed for software engineers, data scientists, and ML engineers looking to move beyond proof-of-concepts and master the engineering rigors of shipping AI products in a production environment.

It is particularly valuable for those aiming for founding or lead AI roles in startups.

Connect with Paul

- Linkedin - https://www.linkedin.com/in/pauliusztin/

- Website - https://www.pauliusztin.ai/

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, Rileen, a Senior Computational Biologist and Cancer Data Scientist, shares his professional journey from physics and computer science to cutting-edge cancer genomics and applied machine learning. From his early work in alternative splicing models to deep learning in medical imaging, Rileen explains how biology, data science, and AI intersect to transform cancer research.

TIMECODES:00:00 Rileen's Career Journey and Education06:14 Understanding Alternative Splicing in Computational Biology10:56 Modeling Alternative Splicing with Machine Learning14:52 Model Error Analysis and Transition to Cancer Research18:37 What Is Cancer? Mutational Theory Explained21:45 Cancer Treatments and Causes24:57 Cancer Genomics and Tumor Models28:59 Comparing Cell Lines and Tumor Samples (Multi-omics Analysis)32:32 Machine Learning Applications in Cancer Research35:38 Deep Learning for Medical Imaging and Pathology39:17 Data Privacy and Applied ML Course Projects42:50 Learning Outcomes and Future Plans46:36 Industry Experience in Pharmaceutical Research50:14 Day in the Life of a Computational Biologist55:02 Advice for Current ML Students58:40 Project Management and Challenges in Genomics1:02:23 Public Data Sets and Cancer Research in GermanyConnect with Rileen:- Twitter - https://x.com/RileenSinha- Linkedin - https://www.linkedin.com/in/rileen-sinha-a644692/- Github - https://github.com/OptimistixConnect 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 session Sofya shares her journey building a pet-tech startup that blends machine learning sensor data and canine behavior analytics. She walks through her path from early programming explorations to launching a health monitoring device designed around anomaly detection and long-term behavioral baselines.

TIMECODES:

00:00 Sofya's pet tech startup with machine learning sensor data and behavior pattern analytics

10:00 Journey from programming hobby to full time software development career

17:20 Career growth after skipping university and building practical experience

24:07 Puppy adoption story and family influence on pet focused innovation

32:16 Dog health monitoring framed as anomaly detection in real world machine learning

37:05 Collecting canine data with emphasis on sleep patterns and cycle tracking

43:35 Establishing a dogs normal baseline through long term data observation

49:34 Startup funding through personal savings and early stage bootstrapping

55:28 Finding cofounders and collaborators through meetups and coworking communities

59:48 Closing insights on Sofya's educational path and early device prototypes

Connect with Sofya

- Website - https://www.fit-tails.com/

- Linkedin - https://www.linkedin.com/in/sofya-yulpatova/

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, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany.

We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market.

TIMECODES:

00:00 Studying Automotive Engineering Across Europe

08:15 How Andrew Ng Sparked a Machine Learning Journey

11:45 Import–Export Work as an Unexpected Career Boos

t17:05 Balancing Family Life with Data Engineering Studies

20:50 From Data Engineer to Head of Data & Cloud

27:46 Building Data Teams & Tackling Tech Debt

30:56 Learning Leadership Through Coaching & Observation

34:17 Management vs. IC: Finding Your Best Fit

38:52 Boosting Developer Productivity with AI Tools

42:47 Succeeding in Germany’s Competitive Data Job Market

46:03 Fast-Track Your Cloud & Data Career

50:03 Mentorship & Supporting Working Moms in Tech

53:03 Cultural & Economic Factors Shaping Women’s Careers

57:13 Top Networking Groups for Women in Data

1:00:13 Turning Domain Expertise into a Data Career Advantage

Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/

- Github - https://github.com/Data-Think-2021

- Website - https://datathinker.de/

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, Anusha Akkina, co-founder of Auralytix, shares her journey from working as a Chartered Accountant and Auditor at Deloitte to building an AI-powered finance intelligence platform designed to augment, not replace, human decision-making. Together with host Alexey from DataTalks.Club, she explores how AI is transforming finance operations beyond spreadsheets—from tackling ERP limitations to creating real-time insights that drive strategic business outcomes.

TIMECODES:

00:00 Building trust in AI finance and introducing Auralytix

02:22 From accounting roots to auditing at Deloitte and Paraxel

08:20 Moving to Germany and pivoting into corporate finance

11:50 The data struggle in strategic finance and the need for change

13:23 How Auralytix was born: bridging AI and financial compliance

17:15 Why ERP systems fail finance teams and how spreadsheets fill the gap

24:31 The real cost of ERP rigidity and lessons from failed transformations

29:10 The hidden risks of spreadsheet dependency and knowledge loss

37:30 Experimenting with ChatGPT and coding the first AI finance prototype

43:34 Identifying finance’s biggest pain points through user research

47:24 Empowering finance teams with AI-driven, real-time decision insights

50:59 Developing an entrepreneurial mindset through strategy and learning

54:31 Essential resources and finding the right AI co-founder

Connect with Anusha

- Linkedin - https://www.linkedin.com/in/anusha-akkina-acma-cgma-56154547/

- Website - https://aurelytix.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/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/

At Qdrant Conference, builders, researchers, and industry practitioners shared how vector search, retrieval infrastructure, and LLM-driven workflows are evolving across developer tooling, AI platforms, analytics teams, and modern search research.

Andrey Vasnetsov (Qdrant) explained how Qdrant was born from the need to combine database-style querying with vector similarity search—something he first built during the COVID lockdowns. He highlighted how vector search has shifted from an ML specialty to a standard developer tool and why hosting an in-person conference matters for gathering honest, real-time feedback from the growing community.

Slava Dubrov (HubSpot) described how his team uses Qdrant to power AI Signals, a platform for embeddings, similarity search, and contextual recommendations that support HubSpot’s AI agents. He shared practical use cases like look-alike company search, reflected on evaluating agentic frameworks, and offered career advice for engineers moving toward technical leadership.

Marina Ariamnova (SumUp) presented her internally built LLM analytics assistant that turns natural-language questions into SQL, executes queries, and returns clean summaries—cutting request times from days to minutes. She discussed balancing analytics and engineering work, learning through real projects, and how LLM tools help analysts scale routine workflows without replacing human expertise.

Evgeniya (Jenny) Sukhodolskaya (Qdrant) discussed the multi-disciplinary nature of DevRel and her focus on retrieval research. She shared her work on sparse neural retrieval, relevance feedback, and hybrid search models that blend lexical precision with semantic understanding—contributing methods like Mini-COIL and shaping Qdrant’s search quality roadmap through end-to-end experimentation and community education.

Speakers

Andrey Vasnetsov

Co-founder & CTO of Qdrant, leading the engineering and platform vision behind a developer-focused vector database and vector-native infrastructure.

Connect: https://www.linkedin.com/in/andrey-vasnetsov-75268897/

Slava Dubrov

Technical Lead at HubSpot working on AI Signals—embedding models, similarity search, and context systems for AI agents.

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

Marina Ariamnova

Data Lead at SumUp, managing analytics and financial data workflows while prototyping LLM tools that automate routine analysis.

Connect: https://www.linkedin.com/in/marina-ariamnova/

Evgeniya (Jenny) Sukhodolskaya

Developer Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content.

Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/

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 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
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• 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/

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/

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:

• Why Orel left a PhD and a simulation‑focused start‑up after Covid hit
• What he learned trying (and failing) to commercialise medical‑imaging simulations
• The first freelance project and the long, quiet months that followed
• How he now finds clients, keeps projects small and delivers value quickly
• Typical work he does for industrial companies: parsing messy machine logs, building simple pipelines, adding structure later
• Favorite everyday tools (Python, DuckDB, a bit of C++) and the habit of blocking time for learning
• Advice for anyone thinking about freelancing: cash runway, networking, and focusing on problems rather than “perfect” tech choices

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

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 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/

🔗 CONNECT WITH WILL

LinkedIn - 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 Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker:

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

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/

Reflection on an Almost Two-Year Journey of Generative AI in Industry – Maria Sukhareva

​About the speaker:

​Maria Sukhareva is a principal key expert in Artificial Intelligence in Siemens with over 15 years of experience at the forefront of generative AI technologies. Known for her keen eye for technological innovation, Maria excels at transforming cutting-edge AI research into practical, value-driven tools that address real-world needs. Her approach is both hands-on and results-focused, with a commitment to creating scalable, long-term solutions that improve communication, streamline complex processes, and empower smarter decision-making. Maria's work reflects a balanced vision, where the power of innovation is met with ethical responsibility, ensuring that her AI projects deliver impactful and production-ready outcomes.

We talked about:

00:00 DataTalks.Club intro

02:13 Career journey: From linguistics to AI

08:02 The Evolution of AI Expertise and its Future

13:10 AI vulnerabilities: Bypassing bot restrictions

17:00 Non-LLM classifiers as a more robust solution

22:56 Risks of chatbot deployment: Reputational and financial

27:13 The role of AI as a tool, not a replacement for human workers

31:41 The role of human translators in the age of AI

34:49 Evolution of English and its Germanic roots

38:44 Beowulf and Old English

39:43 Impact of the Norman occupation on English grammar

42:34 Identifying mushrooms with AI apps and safety precautions

45:08 Decoding ancient languages ​​like Sumerian

49:43 The evolution of machine translation and multilingual models

53:01 Challenges with low-resource languages ​​and inconsistent orthography

57:28 Transition from academia to industry in AI

Join our Slack: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

00:00 DataTalks.Club intro

00:00 Large Hadron Collider and Mentorship

02:35 Career overview and transition from physics to data science

07:02 Working at the Large Hadron Collider

09:19 How particles collide and the role of detectors

11:03 Data analysis challenges in particle physics and data science similarities

13:32 Team structure at the Large Hadron Collider

20:05 Explaining the connection between particle physics and data science

23:21 Software engineering practices in particle physics

26:11 Challenges during interviews for data science roles

29:30 Mentoring and offering advice to job seekers

40:03 The STAR method and its value in interviews

50:32 Paid vs unpaid mentorship and finding the right fit

​About the speaker:

​Anastasia is a particle physicist turned data scientist, with experience in large-scale experiments like those at the Large Hadron Collider. She also worked at Blue Yonder, scaling AI-driven solutions for global supply chain giants, and at Kaufland e-commerce, focusing on NLP and search. Anastasia is a mentor for Ml/AI, dedicated to helping her mentees achieve their goals. She is passionate about growing the next generation of data science elite in Germany: from Data Analysts up to ML Engineers.

Join our Slack: https://datatalks .club/slack.html

We talked about:

00:00 DataTalks.Club intro

02:34 Career journey and transition into MLOps

08:41 Dutch agriculture and its challenges

10:36 The concept of "technical debt" in MLOps

13:37 Trade-offs in MLOps: moving fast vs. doing things right

14:05 Building teams and the role of coordination in MLOps

16:58 Key roles in an MLOps team: evangelists and tech translators

23:01 Role of the MLOps team in an organization

25:19 How MLOps teams assist product teams

27 :56 Standardizing practices in MLOps

32:46 Getting feedback and creating buy-in from data scientists

36:55 The importance of addressing pain points in MLOps

39:06 Best practices and tools for standardizing MLOps processes

42:31 Value of data versioning and reproducibility

44:22 When to start thinking about data versioning

45:10 Importance of data science experience for MLOps

46:06 Skill mix needed in MLOps teams

47:33 Building a diverse MLOps team

48:18 Best practices for implementing MLOps in new teams

49:52 Starting with CI/CD in MLOps

51:21 Key components for a complete MLOps setup

53:08 Role of package registries in MLOps

54:12 Using Docker vs. packages in MLOps

57:56 Examples of MLOps success and failure stories

1:00:54 What MLOps is in simple terms

1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance

Join our Slack: https://datatalks .club/slack.html

We talked about:

00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science

About the speaker:

Rachel is an urban data scientist dedicated to creating liveable cities through the innovative use of data. With a background in geography, and a masters in urban data science, she blends qualitative and quantitative analysis to tackle urban challenges. Her aim is to integrate data driven techniques with urban design to foster sustainable and equitable urban environments. 

Links: - https://datamall.lta.gov.sg/content/datamall/en/dynamic-data.html 00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science Join our slack: https: //datatalks.club/slack.html

We talked about:

00:00 DataTalks.Club intro

00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev

02:29 The founding of DataTalks .Club

03:52 Alexey's transition from Java work to DataTalks.Club

04:58 Growth and success of DataTalks.Club courses

12:04 Motivation behind creating a free-to-learn community

24:03 Staying updated in data science through pet projects

26 :37 Hosting a second podcast and maintaining programming skills

28:56 Skepticism about LLMs and their relevance

31:53 Transitioning to DataTalks.Club and personal reflections

33:32 Memorable moments and the first event's success

36:19 Community building during the pandemic

38:31 AI's impact on data analysts and future roles

42:24 Discussion on AI in healthcare

44:37 Age and reflections on personal milestones

47:54 Building communities and personal connections

49:34 Future goals for the community and courses

51:18 Community involvement and engagement strategies

53:46 Ideas for competitions and hackathons

54:20 Inviting guests to the podcast

55:29 Course updates and future workshops

56:27 Podcast preparation and research process

58:30 Career opportunities in data science and transitioning fields

1:01 :10 Book recommendations and personal reading experiences

About the speaker:

Alexey Grigorev is the founder of DataTalks.Club.

Join our slack: https://datatalks.club/slack.html

We talked about:

00:00 DataTalks.Club intro

08:06 Background and career journey of Katarzyna

09:06 Transition from linguistics to computational linguistics

11:38 Merging linguistics and computer science

15:25 Understanding phonetics and morpho-syntax

17:28 Exploring morpho-syntax and its relation to grammar

20:33 Connection between phonetics and speech disorders

24:41 Improvement of voice recognition systems

27:31 Overview of speech recognition technology

30:24 Challenges of ASR systems with atypical speech

30:53 Strategies for improving recognition of disordered speech

37:07 Data augmentation for training models

40:17 Transfer learning in speech recognition

42:18 Challenges of collecting data for various speech disorders

44:31 Stammering and its connection to fluency issues

45:16 Polish consonant combinations and pronunciation challenges

46:17 Use of Amazon Transcribe for generating podcast transcripts

47:28 Role of language models in speech recognition

49:19 Contextual understanding in speech recognition

51:27 How voice recognition systems analyze utterances

54:05 Personalization of ASR models for individuals

56:25 Language disorders and their impact on communication

58:00 Applications of speech recognition technology

1:00:34 Challenges of personalized and universal models

1:01:23 Voice recognition in automotive applications

1:03:27 Humorous voice recognition failures in cars

1:04:13 Closing remarks and reflections on the discussion

About the speaker:

Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis.

Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.

Join our slack: https://datatalks.club/slack.html

0:00

hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love

0:06

data and we have weekly events and today one is one of such events and I guess we

0:12

are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so

0:19

much because this is the time we usually have uh uh our events uh for our guests

0:27

and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of

0:34

slipped my mind but anyways we have a lot of events you can check them in the

0:41

description like there's a link um I don't think there are a lot of them right now on that link but we will be

0:48

adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget

0:56

to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome

1:02

as the one today and of course very important do not forget to join our community where you can hang out with

1:09

other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click

1:18

on that link ask your question and we will be covering these questions during the interview now I will stop sharing my

1:27

screen and uh there is there's a a message in uh and Christopher is from

1:34

you so we actually have this on YouTube but so they have not seen what you wrote

1:39

but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I

1:46

call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't

1:53

need like you we'll need to focus on answering questions and I'll keep an eye

1:58

I'll be keeping an eye on all the question questions so um

2:04

yeah if you're ready we can start I'm ready yeah and you prefer Christopher

2:10

not Chris right Chris is fine Chris is fine it's a bit shorter um

2:18

okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per

2:25

year but we actually skipped one year so because we did not have we haven't had

2:31

Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and

2:37

head chef or hat cook at data kitchen with 25 years of experience maybe this

2:43

is outdated uh cuz probably now you have more and maybe you stopped counting I

2:48

don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the

2:55

co-author of the data Ops cookbook and data Ops Manifesto and it's not the

3:00

first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one

3:07

will be about data hops so we'll catch up and see what actually changed in in

3:13

these two years and yeah so welcome to the interview well thank you for having

3:19

me I'm I'm happy to be here and talking all things related to data Ops and why

3:24

why why bother with data Ops and happy to talk about the company or or what's changed

3:30

excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always

3:37

thanks Johanna for your help so before we start with our main topic for today

3:42

data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who

3:50

have not heard have not listened to the previous podcast maybe you can um talk

3:55

about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed

4:03

in the last two years so we'll do yeah so um my name is Chris so I guess I'm

4:09

a sort of an engineer so I spent about the first 15 years of my career in

4:15

software sort of working and building some AI systems some non- AI systems uh

4:21

at uh Us's NASA and MIT linol lab and then some startups and then um

4:30

Microsoft and then about 2005 I got I got the data bug uh I think you know my

4:35

kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life

4:41

would be fine um because I was a big you started your own company right and uh it didn't work out that way

4:50

and um and what was interesting is is for me it the problem wasn't doing the

4:57

data like I we had smart people who did data science and data engineering the act of creating things it was like the

5:04

systems around the data that were hard um things it was really hard to not have

5:11

errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my

5:18

Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and

5:24

look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and

5:30

very happy um and if there was I'd have to like rce myself um and you know and

5:36

then the second problem is the team I worked for we just couldn't go fast enough the customers were super

5:42

demanding they didn't care they all they always thought things should be faster and we are always behind and so um how

5:50

do you you know how do you live in that world where things are breaking left and right you're terrified of making errors

5:57

um and then second you just can't go fast enough um and it's preh Hadoop era

6:02

right it's like before all this big data Tech yeah before this was we were using

6:08

uh SQL Server um and we actually you know we had smart people so we we we

6:14

built an engine in SQL Server that made SQL Server a column or

6:20

database so we built a column or database inside of SQL Server um so uh

6:26

in order to make certain things fast and and uh yeah it was it was really uh it's not

6:33

bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's

6:38

still queries um things like that we we uh at the time uh you would use olap

6:43

engines we didn't use those but you those reports you know are for models it's it's not that different um you know

6:50

we had a rack of servers instead of the cloud um so yeah and I think so what what I

6:57

took from that was uh it's just hard to run a team of people to do do data and analytics and it's not

7:05

really I I took it from a manager perspective I started to read Deming and

7:11

think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um

7:18

and so how do you run that factory so it produces things that are good of good

7:24

quality and then second since I had come from software I've been very influenced

7:29

by by the devops movement how you automate deployment how you run in an agile way how you

7:35

produce um how you how you change things quickly and how you innovate and so

7:41

those two things of like running you know running a really good solid production line that has very low errors

7:47

um and then second changing that production line at at very very often they're kind of opposite right um and so

7:55

how do you how do you as a manager how do you technically approach that and

8:00

then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off

8:07

uh with some customers we started building some software and realized that we couldn't work any other way and that

8:13

the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our

8:21

methods and then so yeah we've been in so we've been in business now about a little over 10

8:28

years oh that's cool and uh like what

8:33

uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do

8:41

you remember roughly when devops as I think started to appear like when did people start calling these principles

8:49

and like tools around them as de yeah so agile Manifesto well first of all the I

8:57

mean I had a boss in 1990 at Nasa who had this idea build a

9:03

little test a little learn a lot right that was his Mantra and then which made

9:09

made a lot of sense um and so and then the sort of agile software Manifesto

9:14

came out which is very similar in 2001 and then um the sort of first real

9:22

devops was a guy at Twitter started to do automat automated deployment you know

9:27

push a button and that was like 200 Nish and so the first I think devops

9:33

Meetup was around then so it's it's it's been 15 years I guess 6 like I was

9:39

trying to so I started my career in 2010 so I my first job was a Java

9:44

developer and like I remember for some things like we would just uh SFTP to the

9:52

machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like

10:00

it was not really the I wouldn't call it this way right you were deploying you

10:06

had a Dey process I put it yeah

10:11

right was that so that was documented too it was like put the jar on production cross your

10:17

fingers I think there was uh like a page on uh some internal Viki uh yeah that

10:25

describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is

10:33

why that changed right and and we laugh at it now but that was why didn't you

10:38

invest in automating deployment or a whole bunch of automated regression

10:44

tests right that would run because I think in software now that would be rare

10:49

that people wouldn't use C CD they wouldn't have some automated tests you know functional

10:56

regression tests that would be the exception whereas that the norm at the beginning of your career and so that's

11:03

what's interesting and I think you know if we if we talk about what's changed in the last two three years I I think it is

11:10

getting more standard there are um there's a lot more companies who are

11:15

talking data Ops or data observability um there's a lot more tools that are a lot more people are

11:22

using get in data and analytics than ever before I think thanks to DBT um and

11:29

there's a lot of tools that are I think getting more code Centric right that

11:35

they're not treating their configuration like a black box there there's several

11:41

bi tools that tout the fact that they that they're uh you know they're they're git Centric you know and and so and that

11:49

they're testable and that they have apis so things like that I think people maybe let's take a step back and just do a

11:57

quick summary of what data Ops data Ops is and then we can talk about like what changed in the last two years sure so I

12:06

guess it starts with a problem and that it's it sort of

12:11

admits some dark things about data and analytics and that we're not really successful and we're not really happy um

12:19

and if you look at the statistics on sort of projects and problems and even

12:25

the psychology like I think about a year or two we did a survey of

12:31

data Engineers 700 data engineers and 78% of them wanted their job to come with a therapist and 50% were thinking

12:38

of leaving the career altogether and so why why is everyone sort of unhappy well I I I think what happens is

12:46

teams either fall into two buckets they're sort of heroic teams who

12:52

are doing their they're working night and day they're trying really hard for their customer um and then they get

13:01

burnt out and then they quit honestly and then the second team have wrapped

13:06

their projects up in so much process and proceduralism and steps that doing

13:12

anything is sort of so slow and boring that they again leave in frustration um

13:18

or or live in cynicism and and that like the only outcome is quit and

13:24

start uh woodworking yeah the only outcome really is quit and start working

13:29

and um as a as a manager I always hated that right because when when your team

13:35

is either full of heroes or proceduralism you always have people who have the whole system in their head

13:42

they're certainly key people and then when they leave they take all that knowledge with them and then that

13:48

creates a bottleneck and so both of which are aren aren't and I think the

13:53

main idea of data Ops is there's a balance between fear and herois

14:00

that you can live you don't you know you don't have to be fearful 95% of the time maybe one or two% it's good to be

14:06

fearful and you don't have to be a hero again maybe one or two per it's good to be a hero but there's a balance um and

14:13

and in that balance you actually are much more prod

In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn. 🔗 CONNECT WITH Guillaume Lemaître LinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58 Github - https://github.com/glemaitre Website - https://glemaitre.github.io/ 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email 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 LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/ 🔗 CONNECT WITH ALEXEY Twitter - https://twitter.com/Al_Grigor Linkedin - https://www.linkedin.com/in/agrigorev/ 🎙 ABOUT THE PODCAST At DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds. We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links. You can access all the podcast episodes here - https://datatalks.club/podcast.html 📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you're a company and want to support us, contact at alexey@datatalks.club

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Free LLM course: https://github.com/DataTalksClub/llm-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links. You can access all the podcast episodes here - https://datatalks.club/podcast.html 📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you’re a company, support us at alexey@datatalks.club

We talked about:

• Erum's Background
• Omdena Academy and Erum’s Role There
• Omdena’s Community and Projects
• Course Development and Structure at Omdena Academy
• Student and Instructor Engagement
• Engagement and Motivation
• The Role of Teaching in Community Building
• The Importance of Communities for Career Building
• Advice for Aspiring Instructors and Freelancers
• DS and ML Talent Market Saturation
• Resources for Learning AI and Community Building
• Erum’s Resource Recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/erum-afzal-64827b24/

• Twitter:  https://twitter.com/Erum55449739

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

• Vincent’s Background
• SciKit Learn’s History and Company Formation
• Maintaining and Transitioning Open Source Projects
• Teaching and Learning Through Open Source
• Role of Developer Relations and Content Creation
• Teaching Through Calm Code and The Importance of Content Creation
• Current Projects and Future Plans for Calm Code
• Data Processing Tricks and The Importance of Innovation
• Learning the Fundamentals and Changing the Way You See a Problem
• Dev Rel and Core Dev in One
• Why :probabl. Needs a Dev Rel
• Exploration of Skrub and Advanced Data Processing
• Personal Insights on SciKit Learn and Industry Trends
• Vincent’s Upcoming Projects

Links:

• probabl. YouTube channel: https://www.youtube.com/@UCIat2Cdg661wF5DQDWTQAmg
• Calmcode website: https://calmcode.io/
• probabl. website: https://probabl.ai/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Links:

• Biodiversity and Artificial Intelligence pdf: https://www.gpai.ai/projects/responsible-ai/environment/biodiversity-and-AI-opportunities-recommendations-for-action.pdf

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

• Anahita's Background
• Mechanical Engineering and Applied Mechanics
• Finite Element Analysis vs. Machine Learning
• Optimization and Semantic Reporting
• Application of Knowledge Graphs in Research
• Graphs vs Tabular Data
• Computational graphs
• Graph Data Science and Graph Machine Learning
• Combining Knowledge Graphs and Large Language Models (LLMs)
• Practical Applications and Projects
• Challenges and Learnings
• Anahita’s Recommendations

Links:

• GitHub repo: https://github.com/antahiap/ADPT-LRN-PHYS/tree/main

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

• Tereza’s background
• Switching from an Individual Contributor to Lead
• Python Pizza and the pizza management metaphor
• Learning to figure things out on your own and how to receive feedback
• Tereza as a leadership coach
• Podcasts
• Tereza’s coaching framework (selling yourself vs bragging)
• The importance of retrospectives
• The importance of communication and active listening
• Convincing people you don’t have power over
• Building relationships and empathy
• Inclusive leadership

Links:

• LinkedIn: https://www.linkedin.com/in/tereza-iofciu/
• Twitter: https://twitter.com/terezaif
• Github: https://github.com/terezaif
• Website: https:// terezaiofciu.com

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

• Reem’s background
• Context-aware sensing and transfer learning
• Shifting focus from PhD to industry
• Reem’s experience with startups and dealing with prejudices towards PhDs
• AI interviewing solution
• How candidates react to getting interviewed by an AI avatar
• End-to-end overview of a machine learning project
• The pitfalls of using LLMs in your process
• Mitigating biases
• Addressing specific requirements for specific roles
• Reem’s resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/reemmahmoud/recent-activity/all/
• Website: https://topmate.io/reem_mahmoud

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html