DataTalks.Club

Epizódok

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  Engineering Your Own AI Assistant - Paul Iusztin 24.07.2026 1ó 1p

  In this talk, Paul Iusztin, Creator of Decoding AI and author of the LLM Engineer's Handbook, shares his deep expertise in personal automation from managing a digital life with lightweight data pipelines to architecting autonomous agents for deep research. We explore the mechanics of building personal AI assistants and the critical role of using a "second brain" as a context layer over heavy, over-engineered RAG infrastructure.You’ll learn about:- Organizing your digital life using the PARA method and lightweight data pipelines.- Capturing and retrieving resources effortlessly with Obsidian, Readwise, and custom deep research algorithms.- Leveraging your "second brain" setup as the ultimate context layer for personal AI assistants.- Generating ad-hoc wikis from markdown brain dumps to streamline content creation and research.- Optimizing AI-generated content by deliberately lowering LLM reasoning capabilities for better styling.- Adapting multi-agent workflows and personal wikis to accelerate software engineering and coding tasks.TIMECODES:00:00 Digital life organization using the PARA method and lightweight data pipelines05:21 Seamless resource capture with Obsidian and Readwise09:26 Resource retrieval optimization using a deep research algorithm12:44 High-quality internet curation versus heavy RAG pipelines16:31 Second brain setup as a context layer for personal AI assistants21:40 AI workflow simplification with Anthropic APIs and CLI tools25:26 Ad-hoc wiki generation from markdown brain dumps for content creation29:18 Codebase ingestion and web scraping proxy tool workarounds34:43 Resource reranking and context window management for large texts39:06 Content styling optimization by lowering LLM reasoning capabilities46:18 Multi-agent workflows and personal wikis for software engineering tasks52:32 Personal wiki scaling for enterprise knowledge bases and book writingThis talk is perfect for individual developers, AI engineers, and knowledge workers looking to escape "PoC purgatory" and build practical, low-maintenance personal AI assistants. It offers highly actionable insights for anyone wanting to integrate agentic workflows into their daily productivity systems without over-engineering their tech stack.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/
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  Thriving in the AI Era with Human Skills - Maryam Ramezani-Bartsch 17.07.2026 1ó

  In this talk, Maryam Ramezani-Bartsch, Data and AI Leader with over 20 years of experience at companies like adidas and Zalando, shares her extensive career journey from building foundational ML systems at adidas to coaching data experts through the modern AI landscape. We explore the critical intersection of technical strategy and the essential human skills needed to thrive in the AI era.LINKS:- https://maryamramezani.com/designyourdatacareerYou will learn about:- The surprising similarities and critical differences between the current Generative AI boom and the previous Big Data era.- Why the traditional boundaries between data roles are disappearing and the specific T shaped profile companies are actually hiring for today.- The hidden danger of perfectionism in corporate tech, and what a healthy margin of failure actually looks like in practice.- How to stop leaving your career trajectory to chance by applying product Design Thinking to your own life.- A practical framework for navigating industry uncertainty and tech career anxiety without burning out.- Battle tested strategies for regaining your footing and standing out in a highly competitive job market after a layoff.- The specific non technical human skills that will become your ultimate career moat against automation.TIMECODES:00:00 Human Skills in the AI Era07:07 Building ML Systems at Adidas12:49 Generative AI vs Big Data Era18:39 T-Shaped Data Engineering Roles24:04 Overcoming Perfectionism in Tech30:34 Design Thinking for Data Careers38:59 Managing Tech Career Anxiety45:07 Aligning Passion with Tech Skills50:18 Job Search Strategies After Layoffs56:03 Essential Soft Skills for JuniorsThis talk is essential for data professionals, software engineers, and tech leaders looking to future proof their careers in an increasingly automated world. Whether you are a junior developer navigating a tough job market, an engineer bouncing back from layoffs, or a senior professional looking to strategically design your next pivot, this session provides the tools to build a highly resilient career.Connect with Maryam- Linkedin - https://www.linkedin.com/in/maryam-ramezani-bartsch/- Website - https://maryamramezani.com/- Substack - https://maryamramezani.substack.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/
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  Building a Career in AI From Real Estate to AI Engineering - Gustaf Gyllensporre 10.07.2026 1ó 2p

  In this talk, Gustaf Gyllensporre, Senior AI Engineer and PropTech Founder, shares his unconventional career journey from selling Miami real estate to shipping production AI systems. We explore the tactical steps for breaking into the tech space as a self taught developer and how to successfully bypass traditional industry gatekeepers.Links:-  @PropTechFounder  - https://youtu.be/leXRiJ5TuQo?si=ymK03qKVEC7hAt9N- https://x.com/gostak_ddYou will learn about:- The strategic approach to crafting an AI engineering resume that actually gets noticed by hiring managers.- Why building another generic RAG chatbot might be hurting your portfolio and the specific high impact projects you should build instead.- The massive difference between interviewing for AI roles at dynamic startups versus traditional big tech companies.- How to leverage open source contributions to prove your technical mastery without a computer science degree.- The surprisingly simple networking tactics and developer ambassador programs that can unlock exclusive job opportunities.- Actionable ways to improve the critical social and communication skills that most developers completely ignore.TIMECODES:00:00 AI Engineering Field Guide05:01 Self Taught AI Engineer Pivot09:39 CPython Open Source Contributions13:49 Tech YouTube Channel Growth18:26 AI Engineer Resume Optimization22:44 AI Engineering Portfolio Projects29:23 Startup vs Big Tech Interviews33:47 Open Source AI Project Ideas38:55 Building Deep Research AI Agents42:52 Landing Your First AI Job46:48 Tech Networking Strategies51:16 Technical Project Demo Videos54:53 Self Taught Developer Mistakes58:53 Soft Skills for Software EngineersConnect with Gustaf- Linkedin - https://www.linkedin.com/in/gustaf-g/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/
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  How to Build AI that actually Ships in Production - Aleksandr Kim 03.07.2026 57p

  In this talk, Aleksandr Kim, Senior Data Scientist at Intuit, shares his expertise in building AI-powered features in production from fine-tuning BERT models in cyber security to engineering scalable data verification platforms. We explore the reality of moving beyond messy research code to build observable, cost-effective AI agents and automated pipelines.You’ll learn about:- Translating traditional machine learning metrics into actionable business outcomes- Validating large language model behavior through robust evaluation and alignment techniques- Pivoting from a generic chatbot project to high-value Slack automation workflows- Structuring outputs and guided reasoning layers to eliminate trivial AI summaries- Defining the overlapping skills between AI engineers, data scientists, and full-stack software engineers- Implementing multi-LLM routing logic and token caching to minimize enterprise API expenses- Identifying critical data infrastructure bottlenecks to determine when to pivot or drop an AI pilotTIMECODES:00:00 AI Engineering Production and Scalability06:12 Intuit Ecosystem and QuickBooks Products12:17 Aligning ML Metrics with Business Outcomes18:52 AI Engineers Conducting Customer Interviews25:13 Structured Output and Guided Reasoning31:13 Defining AI Engineering vs Software Engineering37:20 Cost Optimization and Multi LLM Routing43:26 UI Trends and Token Management in Industry49:33 Future Career Trends in AI Engineering55:46 Data Infrastructure Bottlenecks and ML FailuresThis session is designed for mid-to-senior level Data Scientists, Machine Learning Engineers, and Software Engineers who want to develop a highly practical, production-first approach to generative AI. It is especially useful for technology leads focused on reducing token overhead and building self-correcting agentic systems.Connect with Aleksandr- Website - https://alexkimds.github.io/- Linkedin - https://www.linkedin.com/in/aleksandrkim/
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  AI Adoption in Enterprise Beyond Writing Code - Ivan Bilan 26.06.2026 1ó 2p

  In this talk, Ivan, Senior Engineering Manager at Personio, shares his deep expertise in the data and software space from his early days building traditional NLP systems and massive ETL pipelines to his current leadership role in Identity and Access Management (IAM). We explore the rapid evolution of Generative AI, the reality of managing AI agents in production, and the emerging field of context engineering to optimize developer workflows.You’ll learn about:- The buy vs. build dilemma for AI infrastructure and local LLMs.- How AI agents are shifting workloads and evolving code reviews.- Why AI is currently better at fixing tech debt than building from scratch.- Measuring the ROI of AI integration using DORA metrics and cycle times.- Strategies to manage vendor lock-in and minimize AI provider dependency.- Using "context engineering" and specification-driven development to maximize LLM quality.- Why hiring junior engineers is still essential and how AI accelerates their onboarding.TIMECODES:00:00 Career Journey in Data Science and NLP07:37 Industry Adoption of Generative AI and Agents11:45 Buy vs Build Dilemma for AI Infrastructure15:46 AI Capability Limits in Fixing Tech Debt19:32 Developer Workloads and AI Code Contributions24:49 Experimentation with Open Source AI Agent Architectures30:06 Measuring ROI and Business Value of AI Integration35:10 Tracking AI Impact Using DORA Metrics39:51 Impact of AI Code Generation on CI/CD System Reliability43:00 Best Practices for Team AI Tool Adoption48:20 Managing Vendor Lock-In Risks with AI Providers51:27 Importance of Hiring Junior Software Engineers56:28 Accelerated Junior Developer Onboarding with AI Assistants01:00:12 Specification-Driven Development and Context EngineeringThis talk is perfect for software engineers, engineering managers, and technical leaders looking to practically integrate AI tools into their teams without sacrificing code quality or system reliability. It is especially valuable for tech professionals navigating the complexities of AI adoption, CI/CD pipeline management, and organizational scaling in the GenAI era.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/ Connect with Ivan:- LinkedIn - https://www.linkedin.com/in/ivan-bilan/ - Twitter - https://x.com/demiourgosua - Github - https://github.com/ivan-bilan - Website - https://github.com/ivan-bilan
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  Applied AI 2026 Berlin Conference Interview 19.06.2026 54p

  The conference highlighted a critical shift in the technology and engineering ecosystem, moving away from passive implementations toward autonomous AI systems, collaborative communities, and robust engineering guardrails. Discussions centered on the practical architecture required to scale AI safely, the evolution of modern developer tools, and the importance of cross-border technical collaboration. Ultimately, the insights underscored that the future of technology relies on blending rigorous infrastructure with human-centric ecosystem growth.Florian Hönicke an expert in engineering infrastructure, explored the operational shifting of cloud services and the challenges of secure temporary access provisioning. He detailed strategies for managing transient credentials for large groups and autonomous agents using automated serverless functions without exposing long-lived access keys. His central thesis argues that true engineering rigor requires deterministic, self-expiring security layers at the container level.Stella Buhalis, a technical community and developer relations leader, addressed the human dynamics fueling open-source ecosystems and community-driven adoption. She emphasized that long-term project viability stems from structured developer onboarding and lower cognitive barriers rather than pure marketing outreach. Her key insight is that building trusted technical communities acts as the ultimate feedback loop for improving developer experience and software reliability.Błażej Nowakowski, a backend systems architect, focused on database migration paradigms and the optimization of high-dimensional vector search at the network edge. He analyzed real-world infrastructure friction points, specifically isolating SQLite database lock conflicts and remote data sync latencies on serverless architectures. He noted that decoupling persistent remote backends from the core runtime is crucial for maintaining low-latency, multi-cloud application performance.Alena Astrakhantseva, a talent strategy and engineering education specialist, outlined the rapid evolution of technical training as the industry shifts from traditional development to autonomous AI flows. She analyzed how continuous testing, real-time monitoring, and structured evaluation frameworks must become core competencies for new developers. Her notable perspective highlights that the next wave of technical talent must be hired for systemic engineering rigor over simple syntax mastery.Zhen Ming Ng (Babypro), an open-source library maintainer and developer, demonstrated automation workflows for package deployment and baseline library compliance. He focused on minimizing framework overhead by substituting heavy, resource-intensive dependencies with lightweight tokenizers and compact client drivers. His core perspective is that library design must prioritize minimalism to remain functional across edge-native runtime environments.Connect with speakers: Florian HönickeCloud Infrastructure & DevOps Engineer Specialisthttps://www.linkedin.com/in/florian-h%C3%B6nicke-b902b6aaStella BuhalisDeveloper Relations & Technical Community Leadhttps://www.linkedin.com/in/stella-buhalisBłażej NowakowskiBackend Systems Architect & Database Engineerhttps://www.linkedin.com/in/b%C5%82a%C5%BCej-nowakowski-096716168/Alena AstrakhantsevaTechnical Talent Strategist & Engineering Educatorhttps://www.linkedin.com/in/alenaastra/Zhen Ming Ng (Babypro)Open Source Software Maintainer & Core Developerhttps://www.linkedin.com/in/ming91/
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  From GenAI Pilots to Production - Nikita Kozodoi 05.06.2026 1ó 3p

  In this talk, Nikita, Senior Applied Data Scientist at the AWS Generative AI Innovation Center, shares his expertise in bringing enterprise artificial intelligence out of the sandbox—from his early days optimizing traditional machine learning models like gradient boosting to deploying advanced production-grade GenAI pipelines. We explore what it really takes to move generative AI systems from pilot prototypes to production environments.Links:- AWS Generative AI Innovation Center: https://aws.amazon.com/ai/generative-ai/innovation-center/You’ll learn about:- Deploying multi-layered defenses independent of backend LLMs.- Evaluating parameter-efficient methods like LoRA and QLoRA for small models.- Balancing long-term domain expertise with real-time documentation retrieval.- Utilizing multi-agent orchestration for search and anomaly explanation.- Setting up robust LLM-as-a-judge frameworks verified by human metrics.- Leveraging Amazon Bedrock components for memory and runtime scalability.TIMECODES:05:52 Shifting from traditional ML to generative AI07:49 Hybrid pipelines blending classical ML and LLMs11:25 Production guardrails and multi-layered system defense16:15 Prompt bypasses, input attacks, and AI red teaming20:49 Newsletter localization and translation with Zalando27:24 Evaluation frameworks and human-in-the-loop metrics33:07 Aligning LLM-as-a-judge with few-shot prompts34:49 Fine-tuning small language models versus prompting41:18 Complementary mechanics of RAG and fine-tuning43:00 Agentic web search tools for anomaly explanation47:01 Automated text generation from real-time sports sensors49:58 AWS project scoping and proof of concept timelines54:58 Interview requirements and career skills for AWS roles57:59 Enterprise architecture patterns and system observability01:00:42 Reusable infrastructure blocks on Amazon BedrockThis session is designed for machine learning engineers, data scientists, and technical product managers looking to architect reliable, production-ready GenAI workflows. It is highly valuable for teams aiming to bridge the gap between experimental AI prototypes and secure enterprise software.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/ Connect with Nikita- Linkedin - https://www.linkedin.com/in/kozodoi/- Github - https://github.com/kozodoi- Website and blog - https://www.kozodoi.me/
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  From Notebook to Production: Building End-to-End AI Systems - Mariano Semelman 29.05.2026 1ó 7p

  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/
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  Data Makers Fest 2026 Conference Interviews 22.05.2026 1ó 6p

  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
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  Competitions: Beyond the Kaggle Leaderboard - Tatiana Habruseva 01.05.2026 1ó 5p

  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/
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  PyConDE 2026 Conference Interviews 24.04.2026 1ó 22p

  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/⁠
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  Starting a Data Conference: The Data Makers Fest Story - Leonid Kholkine 17.04.2026 1ó 3p

  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/
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  Understanding the AI Engineer Role - Nasser Qadri 10.04.2026 1ó 2p

  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/
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  Data Engineer Career in 2026: Roles, Specializations, and What Companies Look for - Slawomir Tulski 27.03.2026 1ó 8p

  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/
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  Inside the AI Engineer Role: Tools, Skills, and Career Path - Ruslan Shchuchkin 20.03.2026 1ó 7p

  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/
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  How to Become an AI Engineer After a Career Break - Revathy Ramalingam 13.03.2026 47p

  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/
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  The Future of AI Agents - Aditya Gautam 06.03.2026 1ó 8p

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

  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/
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  AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products - Paul Iusztin 06.02.2026 1ó 7p

  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/
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  Applying ML: An Ongoing Personal Journey 09.01.2026 1ó 4p

  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/