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 talked with Abouzar Abbaspour, a data engineer whose career spans software engineering in Iran, building crowd and recommendation systems at a Dutch theme park, deploying large-scale ML models at Bol.com, and now working at Tesla. Abouzar shares how he bridged diverse industries, tackled real-world data challenges, and adapted to new roles while keeping a hands-on approach to machine learning and engineering.TIMECODES00:00 Career journey and early motivations06:17 Moving to Europe for data science12:18 Working with theme parks and crowd modeling18:29 Lessons from ride and visitor data23:06 Building recommendation systems at Efteling27:26 Joining Bol.com and the Dutch e-commerce industry32:49 Product and brand recommendation logic36:09 Experimenting with "Tinder for brands"40:26 Engagement metrics and product validation43:02 From ML engineering to data engineering roles52:04 Hands-on skills at Tesla and industry expectations57:43 Career growth, learning, and adviceConnect with AbouzarLinkedin -   / abouzar-abbaspour  

Website - https://www.abouzar-abbaspour.com/

Connect with DataTalks.Club:

• Join the community - https://datatalks.club/slack.html
• Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...
• Check other upcoming events - https://lu.ma/dtc-events
• GitHub: https://github.com/DataTalksClub
• LinkedIn -   / datatalks-club  
• Twitter -   / datatalksclub  
• Website - https://datatalks.club/

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

TIMECODES

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

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

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

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

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

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

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

54:12 Staying motivated throughout the long-term course

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

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

Connect with Dashel

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

Connect with DataTalks.Club:

• Join the community - https://datatalks.club/slack.html
• Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ
• Check other upcoming events - https://lu.ma/dtc-events
• GitHub: https://github.com/DataTalksClub
• LinkedIn - https://www.linkedin.com/company/datatalks-club/
• Twitter - https://twitter.com/DataTalksClub
• Website - https://datatalks.club/