DataTalks.Club

Episódios

• From Notebook to Production: Building End-to-End AI Systems - Mariano Semelman 29.05.2026 1h 7min

  In this talk, Mariano, Lead Data Scientist and ML Engineer at OLX, shares his journey building high-impact AI media solutions. We explore the transition from traditional e-commerce models to Generative AI and Agentic tools, focusing on how to take AI products from a notebook to full-scale production.You’ll learn about:How to master the full product cycle from requirement gathering to deployment.Using video-to-ad technology to automate car listings and seller experiences.Essential modern tools like FastAPI, Arize, and why UV is a game-changer.When to use LLMs versus specialized vision models like CLIP and YOLO.Why production pipelines are moving from Jupyter notebooks to CLI tools.How agentic coding and AI assistants are 10x-ing development speed.TIMECODES:0:00 Community Introduction and Slack Engagement4:16 Career Journey: From Argentina to Barcelona7:16 Product-Driven AI vs. Traditional Reporting9:41 AI Media Solutions for E-Commerce Sellers10:55 Video-to-Ad: The Future of Marketplaces13:45 Automated Content Creation for Sellers17:10 Defining End-to-End Ownership in Data Science21:12 The Longevity of the CRISP-DM Framework25:33 Impact of Agentic Coding and GitHub Copilot31:42 Why LLMs Aren't Always the Best Solution37:39 Translating Business Needs to ML Requirements41:18 Managing Explicit and Implicit Feedback Loops48:26 Architecture Deep Dive: Image Description Logic55:28 The Declining Role of Notebooks in Production1:02:53 The Modern Tech Stack: Fast API, UV, and ArizeConnect with Mariano: Linkedin - https://www.linkedin.com/in/msemelman/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/
• Data Makers Fest 2026 Conference Interviews 22.05.2026 1h 6min

  At Data Makers Fest, a recurring theme was the tension between GenAI hype and production reality. Speakers stressed that classical ML, MLOps, evaluation, data quality, and governance remain essential—especially in regulated sectors like fintech and healthcare. Another strong theme was inclusivity: building AI that serves smaller languages, diverse communities, and practitioners beyond the English-centric ecosystem.Ryan Chaves. Head of ML at a Dutch fintech, Ryan focused on the gap between AI demos and production systems. He argued that classical ML remains critical for fraud detection and risk scoring, while GenAI works best as an accelerator on top of existing systems. He also emphasized storytelling, stakeholder communication, and mentorship as core engineering skills.Alp Öktem. Computational linguist and researcher Alp explored the imbalance between AI progress in English and low-resource languages. Through Mozilla Data Collective, he highlighted how open datasets, speech corpora, and synthetic data can expand AI access to underrepresented communities. His broader warning: fluent AI can still fail culturally, linguistically, and ethically.Agnieszka Kamińska. Working in pharmaceutical ML engineering, Agnieszka discussed extracting scientific knowledge from research documents into knowledge graphs. Her focus was reliability: LLMs help with entity extraction and relationship discovery, but trustworthy systems still require ontologies, validation layers, and production-minded engineering. She advocated a pragmatic middle ground between AI hype and skepticism.Nemanja Radojković. An MLOps engineer in finance, Nemanja reflected on how GenAI is changing software engineering itself. He argued that coding assistants improve productivity but risk weakening engineers’ understanding if overused. His central point: governance, reproducibility, and platform engineering will become even more important as organizations deploy AI agents at scale.Filipa Castro. Leading AI initiatives at Euronext, Filipa described how GenAI is integrated into regulated financial workflows. Her team uses LLMs to automate document-heavy operational processes while preserving human validation. Her broader message: successful enterprise AI depends less on flashy models and more on infrastructure foundations like CI/CD, monitoring, governance, and operational rigor.Beatriz Silva. As a student volunteer pursuing a master’s in data science, Beatriz represented the conference’s educational and community dimension. For her, the event was about access—networking with companies, exploring thesis opportunities, and connecting academic learning with industry practice. Her perspective highlighted how conferences like Data Makers Fest help shape the next generation of AI practitioners.Connect with speakers: Ryan Chaves. Head of Machine Learning at a Dutch fintech focused on fraud detection, risk systems, and production ML. LinkedInAlp Öktem. Computational linguist and researcher focused on low-resource languages, inclusive AI, and open language datasets. LinkedInAgnieszka Kamińska. Machine Learning Engineer working on scientific knowledge extraction, knowledge graphs, and AI systems in pharma. LinkedInNemanja Radojković. Senior MLOps Engineer specializing in regulated financial systems, AI governance, and platform engineering. LinkedInFilipa Castro. AI Lead at Euronext focused on enterprise GenAI systems, operational AI strategy, and financial services automation. LinkedInBeatriz Silva. Data science master’s student and conference volunteer exploring opportunities in ML and computer vision. LinkedIn
• Competitions: Beyond the Kaggle Leaderboard - Tatiana Habruseva 01.05.2026 1h 5min

  In this talk, Tatiana, Staff Software Engineer at LinkedIn, shares her journey from academic physics to becoming a Kaggle Master and winning the Sound Demixing Challenge. We explore how to use machine learning competitions as a strategic tool to build a high-impact career and bridge the gap between theory and production.You’ll learn about:Turning competition code into professional GitHub repos.Converting results into papers for NIPS and CVPR.How LLMs are changing the benchmark for AI competitions.Why hands-on implementation beats passive learning.Using Topcoder and AI Crowd for research-driven goals.Practical steps for your very first model submission.Links:Rise: 3 Practical Steps for Advancing Your Career, Standing Out as a Leader, and Liking Your Life. By Patty Azzarello https://www.porchlightbooks.com/pages/author/Patty_Azzarello-16156396 - awesome book about why doing good is not enough, and what else you need to do to promote your career (same applies to competitions)AICrowd - https://www.aicrowd.com/challenges Grand challenges - https://grand-challenge.org/challenges/Kaggle competitions - https://www.kaggle.com/competitionsTopCoder challenge SpaceNet 9 - https://www.topcoder.com/challenges/9620f66a-767e-40ac-81d5-5cc61274b186(no current active competitions, but they appear)Medium blog post with instruction - https://medium.com/data-science/writing-papers-tech-reports-after-kaggle-competitions-ee504fc0c4c1Kaggle Solution Write-Up Documentation - https://www.kaggle.com/solution-write-up-documentationEvaluating Machine Learning Agents on Machine Learning Engineering - https://arxiv.org/abs/2410.07095Machine Learning Engineering Agent via Search and Targeted Refinement - https://arxiv.org/html/2506.15692v2AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench - chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2507.02554TIMECODES:00:00 Tatiana’s journey from academia to staff software engineer06:01 Machine learning applications in physics and signal processing09:13 Skill development and domain diversification on Kaggle13:35 Agentic AI benchmarks and automated competition entries17:43 Deep technical mastery versus leaderboard gamification23:04 Hands-on implementation and the illusion of learning26:01 Specialized platforms and fair competition environments31:35 Academic publications and research from silver medals35:24 GitHub repositories and engineering portfolio building39:02 Technical marketing via blog posts and LinkedIn43:25 Innovative approaches for academic conference submissions47:21 Research challenges at NIPS and CVPR workshops52:51 Medical imaging platforms and specialized recommendations57:46 First submission strategies for beginners01:00:56 Asynchronous collaboration and competition team dynamicsPerfect for data scientists and engineers looking to transition from academia or build a formal portfolio using Kaggle as a career-advancement tool.Connect with Tatiana:Linkedin - https://www.linkedin.com/in/tatigabru/
• PyConDE 2026 Conference Interviews 24.04.2026 1h 22min

  At PyConDE 2026, community leaders, educators, and Python tooling builders explored how Python is evolving in the age of AI — and why human connection, mentorship, and strong fundamentals matter more than ever.Jessica Greene (Ecosia / PyLadies Berlin) spoke about her work as a machine learning engineer and community organizer. She highlighted PyLadies Berlin’s role in creating inclusive spaces for learning, networking, and career growth, and emphasized that AI should be seen as an amplification tool—not a replacement for solid engineering or people skills.Cheuk Ting Ho (JetBrains) discussed her role on the PyCharm team, where conferences are key for gathering feedback and staying connected to the community. She shared insights from her talk on free-threaded Python and her approach to technical storytelling across talks, blogs, videos, and informal interviews.Sebastian Raschka reflected on his work as an AI educator focused on “from scratch” explanations of machine learning and LLMs. Driven by curiosity, he prefers creating new talks over repeating old ones and aims to help people understand what happens under the hood—especially with reasoning models.Kyle Into (Meta) introduced Pyrefly, a Rust-based Python type checker designed for large codebases. He explained how type checking improves both human and AI-assisted development by making interfaces explicit, reducing risk, and strengthening project structure.Valerio Maggio shared his journey from data science into developer advocacy and community organizing. He emphasized that conferences rely on volunteers, that lightning talks boost accessibility and energy, and that sustainable processes are essential to avoid burnout.Tereza Iofciu discussed her “Data Diplomat” coaching framework, helping data professionals navigate leadership and uncertainty. She noted that AI and lean teams are raising expectations, making it crucial to think strategically, build fundamentals, and invest in real networks.Irina Saribekova described her transition from organizing Python events in Saint Petersburg to supporting PyData Berlin and PyConDE. She highlighted that conferences are built on trust, relationships, and clear systems—and that developer relations extends this work through talks, writing, and community engagement.Jessica GreeneMachine Learning Engineer at Ecosia, PyLadies Berlin co-organizer, and chair of the PyLadies Germany fund.Connect: ⁠https://www.linkedin.com/in/jessica0greene/⁠Cheuk Ting HoDeveloper Advocate at JetBrains working with the PyCharm team and active in the global Python community.Connect: ⁠https://www.linkedin.com/in/cheukting-ho/⁠Sebastian RaschkaAI educator, author, and machine learning researcher focused on LLMs, reasoning models, and educational “from scratch” implementations.Connect: ⁠https://www.linkedin.com/in/sebastianraschka/⁠Kyle IntoEngineer at Meta working on Pyrefly, a fast Python type checker built for large-scale codebases and AI-assisted development.Connect: ⁠https://www.linkedin.com/in/kyleinto/⁠Valerio MaggioData scientist, developer advocate, community organizer, and long-time contributor to PyCon Italia andPyConDE.Connect: ⁠https://www.linkedin.com/in/valeriomaggio/⁠Tereza IofciuData coach, trainer, community contributor, and creator of the Data Diplomat framework for data professionals and leaders.Connect: ⁠https://www.linkedin.com/in/tereza-iofciu/⁠Irina SaribekovaDeveloper relations specialist and Python community organizer involved in PyData Berlin, PyConDE, and conference community building.Connect: ⁠https://www.linkedin.com/in/irinasaribekova/⁠
• Starting a Data Conference: The Data Makers Fest Story - Leonid Kholkine 17.04.2026 1h 3min

  In this talk, Leonid Kholkine, Head of Research & Development at Their Data and Co-founder of Data Makers Fest, shares his unique journey from leading international student organizations to building one of Europe’s premier data conferences. We explore the behind-the-scenes reality of community building, the evolution of the Portuguese data scene, and the technical challenges of managing AI observability at an enterprise scale.You’ll learn about:- Understanding the hybrid role between product engineering and high-touch consultancy.ow organizing meetups and leagues creates a professional reputation and high-trust networks.- The hidden complexities of moving from local meetups to large-scale international conferences (venues, AV, and timing).- How Leonid used custom code and embeddings to automate speaker scheduling and timetable optimization.- Why community is the essential antidote for data practitioners working as the "only one" in their company.- A look into R&D at Their Data and the future of monitoring and self-improving generative AI workflows.Links: - www.datamakersfest.com- Data Lead Club - http://dataleadclub.ripply.net/- DareData - https://www.daredata.ai/- GenOS by DareData - https://www.daredata.ai/gen-osTIMECODES:00:00 Community Building in Data and AI03:02 Computer Engineering and International Leadership Roots06:13 Machine Learning Research in Sports Physiology10:18 Data Lead Club and Executive Networking Retreats14:03 AI Observability and R&D at Their Data18:50 Professional Growth through Community Organizing22:11 The Origins of Data Science Portugal27:57 Logistical Challenges of In-Person Conferences31:24 Strategic Event Scheduling and Venue Selection36:52 Automated Timetable Optimization with Custom Code41:22 Curating Quality Speaker Proposals in the AI Era45:08 Sponsorship Value and Student Ticket Accessibility50:23 Partnership Outreach and Network Development54:44 The Forward Deployed Engineer Role and Methodology58:35 Professional Development for Junior Data ScientistsThis video is a must-watch for data practitioners, aspiring community leaders, and event organizers. It provides deep value for anyone looking to understand the intersection of technical R&D and the "human stack" of networking and professional development.Connect with Leonid- Linkedin - https://www.linkedin.com/in/kholkine/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/
• Understanding the AI Engineer Role - Nasser Qadri 10.04.2026 1h 2min

  In this talk, Nasser Qadri, AI Engineering Manager at Google, shares his unique career journey—from a PhD in Politics and International Relations to leading high-stakes AI initiatives. We explore the evolution of the AI Engineer role, the critical intersection of social science and machine learning, and how to build robust agentic workflows with engineering rigor.You’ll learn about:- Moving beyond simple API calls to implementing full-stack engineering principles and "Agent Ops."- How a background in qualitative research and statistics provides a unique "moral compass" for building ethical AI.- A strategic roadmap for transitioning from non-traditional backgrounds into elite AI engineering roles.- Using design thinking and personal "pain points" to drive meaningful technical innovation.- Why traditional ML and model distillation will remain vital as we move from generalist LLMs to specialized, high-speed agents.- How to navigate the complex landscape of AI frameworks and build depth in your technical stack.TIMECODES:00:00 Transitioning from Social Science to Software Engineering07:45 Applying Statistical Rigor to Generative AI Evaluation12:10 Balancing Research Mindsets with Engineering Speed16:30 Managing Non-Deterministic Systems and Model Creativity20:15 Comparing AI Roles in Big Tech vs Startups24:40 Learning by Building: Solving Personal Pain Points31:50 Mental Frameworks for Problem Finders and Solvers36:15 Human-Centered Design in the Age of LLMs42:05 Beyond API Calls: Software Engineering Rigor for Agents45:50 Orchestration and the Rise of Agent Ops51:30 Depth vs Breadth in AI Framework Selection56:10 The Future of Latency and Traditional ML Integration1:01:20 When to Prioritize Model Distillation and Fine-Tuning1:02:10 Closing Thoughts and Future OutlookThis conversation is designed for software engineers, data scientists, and career-switchers looking to transition into the Generative AI space. It is particularly valuable for technical leaders in large organizations and startups who need to balance rapid AI prototyping with long-term system reliability.Connect with Nasser- Linkedin - https://www.linkedin.com/in/nasserq/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/
• Data Engineer Career in 2026: Roles, Specializations, and What Companies Look for - Slawomir Tulski 27.03.2026 1h 8min

  In this talk, Slawomir Tulski, Data Leadership Consultant and former Meta Data Engineering Manager, shares his ten-year journey through the evolution of data systems—from researching glaciers in Poland to scaling the ads ranking infrastructure at one of the world's largest tech giants. We explore the shifting definition of the Data Engineer, the "Actionable Data" philosophy, and how to navigate the 2026 hiring market amidst the rise of AI.You’ll learn about:- How to distinguish between Platform DE, Product DE, and Analytics Engineering.- Why most teams over-engineer their stacks and how to build "Value-First" instead of "Tool-First."- Why being "cloud-cost-conscious" is the most underrated competitive advantage in modern data teams.- How to identify "Legacy Traps" and choose a company culture that fosters growth.- Why strategic builders will thrive while "DBT Monkeys" and manual triaging roles are at risk of automation.- How to frame side projects and end-to-end "Toy Platforms" to stand out to recruiters without a Big Tech pedigree.TIMECODES:00:00 From Measuring Glaciers to London’s Tech Scene06:47 Hadoop vs. AI: Lessons from the Original Big Data Hype11:54 The Data Identity Crisis: Platform vs. Product Engineering17:29 Tech-Native vs. Tech-by-Necessity Company Cultures25:33 The Competitive Advantage of Cost-Aware Engineering30:56 Avoiding Over-Engineered Platforms and Modern Data Stacks38:01 The Real-Time Myth: When to Use Kafka and Spark42:08 Breaking into Data Engineering: 2026 Market Reality51:04 AI Automation: Why Strategic Builders Outlast "DBT Monkeys"57:35 Portfolio Strategy: Framing Side Projects for Maximum Impact1:04:42 The Ultimate Portfolio Project: Building End-to-End Platforms1:07:49 Networking Advice and Local Gdansk CultureThis talk is designed for ambitious data professionals including engineers, analysts, and career-switchers who want a pragmatic, "fluff-free" roadmap for surviving and thriving in the 2026 data landscape. It is particularly valuable for hiring managers and senior leaders looking to audit their recruitment processes, as well as those in traditional corporate environments seeking to implement the agile, high-impact engineering cultures found in Big Tech giants like Meta.Connect with Slawomir:- Linkedin - https://www.linkedin.com/in/slawomir-tulski-091611116/- Form for DE role Ebook - https://docs.google.com/forms/d/e/1FAIpQLSdSCLaBdTtuRlgV_nukKckumR60VOovECtlRIRI5DMUIk36EQ/viewform?usp=dialogConnect 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/
• Inside the AI Engineer Role: Tools, Skills, and Career Path - Ruslan Shchuchkin 20.03.2026 1h 7min

  In this talk, Ruslan Shchuchkin, GenAI Engineer at Finance Guru, shares his unique career evolution from business administration and account management to building production-grade generative AI systems. We explore the transition from traditional Data Science to the modern AI Engineer role, defined by the "universal soldier" mindset and the ability to ship end-to-end products.You’ll learn about:- Why modern AI engineers must bridge the gap between frontend, backend, and LLM logic.- How building in public and creating personal projects like Branch GPT can fast-track your hiring process.- Why understanding human behavior and user needs is the ultimate safeguard against AI replacement.- How to use tools like Cursor and Claude to accelerate development without losing your technical edge.- How traditional roles are evolving and why evaluation is the new superpower for data professionals.- Practical tips for starting local AI meetups and side hustles (like the Catch a Flat extension) without perfectionism.- Why the industry is shifting toward specific project track records and energy over formal degrees.Links: - https://www.swyx.io/create-luckTIMECODES:00:00 From Account Management to Data Science07:51 Building Branch GPT and Side Project Philosophy10:41 Transitioning to AI Engineering Full-Time15:26 Maximizing Your "Luck Surface Area"19:48 The AI Engineer as a Universal Soldier23:19 Humans vs. AI in Product Discovery28:31 Staying Sharp with X, Grok, and Meetups33:21 How to Launch a Lean Local AI Community38:49 Catch a Flat: Vibe Coding and Side Hustles43:04 Learning the Business Side through Small Projects48:48 Sourcing Project Inspiration from Daily Life52:28 The Future and Longevity of Data Science57:39 Skills over Degrees: The Realities of Hiring01:03:12 Using AI to Learn Instead of Just CodingThis talk is for Data Scientists and Software Engineers looking to transition into AI Engineering or GenAI roles. It is equally valuable for developers interested in building side projects, maximizing their career visibility, and staying updated in a rapidly shifting tech landscape.Connect with Ruslan- Linkedin - https://www.linkedin.com/in/ruslanshchuchkin/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/
• How to Become an AI Engineer After a Career Break - Revathy Ramalingam 13.03.2026 47min

  In this episode Revathy Ramalingam, Senior Software Engineer and AI Engineer at a healthcare startup, shares her inspiring personal journey from over nine years in telecom software architecture to successfully transitioning back into the industry after a seven-year career break. We explore the evolution of the AI engineer role, the practical application of RAG pipelines, and the strategic use of AI tools to rebuild a technical career.You'll learn about:- AI Career Mapping: Using LLMs to design an upskilling roadmap.- Vibe Coding: Leveraging AI tools for rapid prototyping.- RAG Implementation: Building retrieval systems with LangChain.- Interview Strategy: Proving technical skills after a career gap.- Learning in Public: Building a network through community projects.TIMECODES:00:00 Why Move to AI? Using ChatGPT to Plan a Career Pivot11:00 Learning in Public: The Power of Community Support15:35 Telecom Capstone: Predicting Network Slices with ML22:15 "Vibe Coding" & Building Prototypes with AI Dev Tools28:00 The Interview Process: Navigating a 7-Year Career Break33:45 Practical Interview Tasks: Building a PDF Q&A Assistant39:40 Career Advice: Clear Plans, AI Mentors, and Hard Work44:30 Closing Thoughts: Scaling the Learning LadderThis talk is for developers and career-changers looking for a blueprint to enter the AI engineering space. It is ideal for those interested in RAG, healthcare tech, and practical career resets.Connect with Revathy- Github - https://github.com/RevathyRamalingam- Linkedin - https://www.linkedin.com/in/revathy-ramalingam/ 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/
• The Future of AI Agents - Aditya Gautam 06.03.2026 1h 8min

  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/
• Foundations of Analytics Engineer Role: Skills, Scope, and Modern Practices - Juan Manuel Perafan 27.02.2026 1h 23min

  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-zoomcampLuma: https://luma.com/0uf7mmupTIMECODES:0:00 Juan’s psychological research and transition to data4:36 Riding the wave: The early days of analytics engineering7:56 Breaking down the gap between analysts and engineers11:03 The art of turning business reality into clean data16:25 Why data engineering is about safety, not just speed20:53 Reimagining data modeling in the modern era26:53 To split or not to split: Finding the right team roles30:35 Python, SQL, and the technical toolkit for success38:41 How to stop manually testing your data dashboards46:34 Bringing software engineering rigor to data workflows49:50 Must-read books and resources for mastering the craft55:42 The future of dbt and the shifting tool landscape1:00:29 Deciphering the lakehouse: Warehousing in the cloud1:11:16 Pro-tips for starting your data engineering journey1:14:40 The big debate: Databricks vs. Snowflake1:18:28 Why every data professional needs a local communityThis 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 JuanLinkedin - https://www.linkedin.com/in/jmperafan/ Website - https://juanalytics.com/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/
• AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products - Paul Iusztin 06.02.2026 1h 7min

  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 AI07:08 Deep learning and the autonomous driving challenge12:09 The transition to global product engineering15:13 Survival guide: Data science vs. AI engineering22:29 The full-stack AI engineer skill stack29:12 Mastering RAG and knowledge management32:27 The generalist edge: Learning with AI42:21 Technical pillars for shipping AI products54:05 Portfolio secrets and the "second brain"58:01 The future of the LLM engineer’s handbookThis 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/
• Applying ML: An Ongoing Personal Journey 09.01.2026 1h 4min

  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/
• Building Pet Health Tech: ML, Sensors, and Dog Behavior Data 12.12.2025 1h 1min

  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 analytics10:00 Journey from programming hobby to full time software development career17:20 Career growth after skipping university and building practical experience24:07 Puppy adoption story and family influence on pet focused innovation32:16 Dog health monitoring framed as anomaly detection in real world machine learning37:05 Collecting canine data with emphasis on sleep patterns and cycle tracking43:35 Establishing a dogs normal baseline through long term data observation49:34 Startup funding through personal savings and early stage bootstrapping55:28 Finding cofounders and collaborators through meetups and coworking communities59:48 Closing insights on Sofya's educational path and early device prototypesConnect 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/
• From Full-Time Mom to Head of Data and Cloud - Xia He-Bleinagel 28.11.2025 1h 2min

  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 Europe08:15 How Andrew Ng Sparked a Machine Learning Journey11:45 Import–Export Work as an Unexpected Career Boost17:05 Balancing Family Life with Data Engineering Studies20:50 From Data Engineer to Head of Data & Cloud27:46 Building Data Teams & Tackling Tech Debt30:56 Learning Leadership Through Coaching & Observation34:17 Management vs. IC: Finding Your Best Fit38:52 Boosting Developer Productivity with AI Tools42:47 Succeeding in Germany’s Competitive Data Job Market46:03 Fast-Track Your Cloud & Data Career50:03 Mentorship & Supporting Working Moms in Tech53:03 Cultural & Economic Factors Shaping Women’s Careers57:13 Top Networking Groups for Women in Data1:00:13 Turning Domain Expertise into a Data Career AdvantageConnect 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/
• From Black-Box Systems to Augmented Decision-Making - Anusha Akkina 28.11.2025 1h 2min

  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 Auralytix02:22 From accounting roots to auditing at Deloitte and Paraxel08:20 Moving to Germany and pivoting into corporate finance11:50 The data struggle in strategic finance and the need for change13:23 How Auralytix was born: bridging AI and financial compliance17:15 Why ERP systems fail finance teams and how spreadsheets fill the gap24:31 The real cost of ERP rigidity and lessons from failed transformations29:10 The hidden risks of spreadsheet dependency and knowledge loss37:30 Experimenting with ChatGPT and coding the first AI finance prototype43:34 Identifying finance’s biggest pain points through user research47:24 Empowering finance teams with AI-driven, real-time decision insights50:59 Developing an entrepreneurial mindset through strategy and learning54:31 Essential resources and finding the right AI co-founderConnect 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/
• Qdrant 2025 Conference Interviews 28.11.2025 51min

  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.SpeakersAndrey VasnetsovCo-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 DubrovTechnical 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 AriamnovaData 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) SukhodolskayaDeveloper Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content.Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/
• How to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-Anderson 24.10.2025 1h 1min

  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 adoptionPractical prompting techniques for accurate timestamp and data generationBuilding and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoringTools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)The evolution of AI agents—from simple RAG systems to proactive, embedded assistantsHow to escape “proof of concept purgatory” and prioritize AI projects that drive business valueStep-by-step guidance for building reliable, evaluable AI agentsThis session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you’re optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.LINKSEscaping 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-agentsHow to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.beMy Vanishing Gradients Substack - https://hugobowne.substack.com/Building LLM Applications for Data Scientists and Software Engineers https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclubTIMECODES:00:00 Introduction and Expertise04:04 Transition to Freelance Consulting and Advising08:49 Restructuring Teams and Incentivizing AI Adoption12:22 Improving Prompting for Timestamp Generation17:38 Evaluation Sets and Failure Analysis for Reliable Software23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets27:38 Software Tools for Evaluation and Monitoring33:14 Evolution of AI Tools: Proactivity and Embedded Agents40:12 The Future of AI is Not Just Chat44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value50:19 RAG vs. Agents: Complexity and Power Trade-Offs56:21 Recommended Steps for Building Agents59:57 Defining Memory in Multi-Turn ConversationsConnect with HugoTwitter - https://x.com/hugobowneLinkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/Github - https://github.com/hugobowneWebsite - https://hugobowne.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
• From Biotechnology to Bioinformatics Software - Sebastian Ayala Ruano 24.10.2025 55min

  In this talk, Sebastian, a bioinformatics researcher and software engineer, shares his inspiring journey from wet lab biotechnology to computational bioinformatics. Hosted by Data Talks Club, this session explores how data science, AI, and open-source tools are transforming modern biological research — from DNA sequencing to metagenomics and protein structure prediction.You’ll learn about: - The difference between wet lab and dry lab workflows in biotechnology - How bioinformatics enables faster insights through data-driven modeling - The MCW2 Graph Project and its role in studying wastewater microbiomes - Using co-abundance networks and the CC Lasso algorithm to map microbial interactions - How AlphaFold revolutionized protein structure prediction - Building scientific knowledge graphs to integrate biological metadata - Open-source tools like VueGen and VueCore for automating reports and visualizations - The growing impact of AI and large language models (LLMs) in research and documentation - Key differences between R (BioConductor) and Python ecosystems for bioinformaticsThis talk is ideal for data scientists, bioinformaticians, biotech researchers, and AI enthusiasts who want to understand how data science, AI, and biology intersect. Whether you work in genomics, computational biology, or scientific software, you’ll gain insights into real-world tools and workflows shaping the future of bioinformatics.Links:- MicW2Graph: https://zenodo.org/records/12507444- VueGen: https://github.com/Multiomics-Analytics-Group/vuegen- Awesome-Bioinformatics: https://github.com/danielecook/Awesome-BioinformaticsTIMECODES00:00 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 EcuadorConnect with SebastianTwitter - https://twitter.com/sayalaruanoLinkedin - https://linkedin.com/in/sayalaruano Github - https://github.com/sayalaruanoWebsite - https://sayalaruano.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
• Lessons from Applied AI: Tesla, Waymo, and Beyond - Aishwarya Jadhav 10.10.2025 59min

  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/