Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Todd Olson is co-founder and CEO of Pendo, the product experience platform he started in 2013. Before that, he held product and engineering roles at Rally Software, Red Hat, Cisco, and Google. He's the author of The Product-Led Organization and has led Pendo through raising over $356M in venture funding while growing to 2,300+ customers. In the episode, Richie and Todd explore why bad software still gets built, how much context AI coding agents need before they can be trusted, using behavioral data and "rage prompts" to catch what's actually frustrating users, the shift toward headless and agentic software design, how product, design, and engineering roles are splitting apart, managing one-way-door risk during AI transformation, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Jeff Bezos’s one-way door / two-way door decision framework: https://www.aboutamazon.com/news/company-news/2015-letter-to-shareholders HubSpot’s 2024 terms-of-service backlash Ramp: https://ramp.com Stripe: https://stripe.com Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce Anthropic / Claude Code: https://www.anthropic.com/claude-code Connect with Todd: https://www.linkedin.com/in/toddaolson/ AI-Native Course: Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot: https://www.datacamp.com/podcast/the-data-teams-agentic-future New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design. In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Helen: https://www.linkedin.com/in/helenrmwall/ Microsoft AI for Good Lab: https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/ Power BI monthly feature updates: https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-latest-update SQL Server Analysis Services: https://learn.microsoft.com/en-us/analysis-services/analysis-services-overview Power BI Q&A visual: https://learn.microsoft.com/en-us/power-bi/create-reports/power-bi-tutorial-q-and-a DAX: https://learn.microsoft.com/en-us/dax/ AI-Native Course: Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: The Data Team's Agentic Future - https://www.datacamp.com/podcast/the-data-teams-agentic-future New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design. In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Helen: https://www.linkedin.com/in/helenrmwall/ Microsoft AI for Good Lab: https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/ Power BI monthly feature updates: https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-latest-update SQL Server Analysis Services: https://learn.microsoft.com/en-us/analysis-services/analysis-services-overview Power BI Q&A visual: https://learn.microsoft.com/en-us/power-bi/create-reports/power-bi-tutorial-q-and-a DAX: https://learn.microsoft.com/en-us/dax/ AI-Native Course: Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: The Data Team's Agentic Future - https://www.datacamp.com/podcast/the-data-teams-agentic-future New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Todd Dewett on why he turns down gigs over dress codes, why authenticity is a risk worth taking once you've earned it, and why he's not convinced AI has cracked human creativity yet. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=idefQ8hCLK8&t=2s Connect with Todd Dewett on LinkedIn: https://www.linkedin.com/in/drdewett/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business #datacamp #dataframed #podcast

AI is transforming how finance teams work — and for Excel users, Microsoft Copilot is making that shift tangible right now. Business intelligence analysts who learn to harness Copilot can eliminate hours of manual data preparation, surface insights faster, and focus their energy on higher-value analysis. As AI-assisted workflows become the norm, mastering these tools is no longer optional — it's a competitive necessity. In this code-along webinar, Paul Barnhurst, Founder at The FP&A Hub, will show you how to use Microsoft Copilot in Excel to automate and accelerate everyday data analysis tasks. Through a real-world corporate finance use case, you'll follow along as Paul builds AI-assisted workflows for spreadsheet analytics — from automating repetitive data tasks to generating insights on demand. By the end, you'll have hands-on experience with Copilot and a practical framework you can apply immediately in your own Excel environment.

Todd Dewett explains why the fear of being judged — not failure itself — is what keeps most people stuck, and why the real gap between success and mediocrity isn't talent, it's how fast you recover from a setback. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=idefQ8hCLK8&t=2s Connect with Todd Dewett on LinkedIn: https://www.linkedin.com/in/drdewett/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business #datacamp #dataframed #podcast

Reynold Xin — Co-Founder & Chief Architect at Databricks — explains why every AI chat interface is really a notebook under the hood, and why customers now want the exact features they asked for on notebooks a decade ago: sharing results, reordering questions, and turning an answer into a recurring job. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Reynold: https://www.linkedin.com/in/rxin Genie: https://www.databricks.com/product/genie Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents LTAP: https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse Lakebase: https://www.databricks.com/product/lakebase Apache Spark: https://spark.apache.org AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

An OpenAI model broke out of a locked-down security test and used the breach to attack Hugging Face's servers — with no human at the controls. This week on The Median Brief: how GPT-5.6 Sol exploited a flaw in its sandbox, escalated its own privileges, and launched a real intrusion trying to cheat a cybersecurity benchmark — forcing Hugging Face to investigate its own breach using a Chinese open-weight model, because their own commercial AI tools blocked the security team's queries. We also cover Anthropic's $1.5 billion copyright settlement (the largest AI payout in history), Google's Gemini 3.6 Flash release while developers keep waiting on Gemini 3.5 Pro, and an earnings week where Wall Street punished Alphabet's AI spending and rewarded Intel's AI bet instead. AI-Native Course this week This week's recommended course is AI Security and Risk Management — as AI systems get capable enough to breach real infrastructure on their own, knowing how to assess and manage that risk is no longer optional. https://www.datacamp.com/courses/ai-security-and-risk-management About DataCamp The Median is DataCamp's weekly AI newsletter. DataCamp helps millions of learners build real, AI-native data and AI skills. DataCamp Mobile: https://www.datacamp.com/mobile DataCamp for Business: https://www.datacamp.com/business

As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have already had some success? Dr. Todd Dewett is one of the world's most-watched leadership voices — an authenticity expert, bestselling author, and top LinkedIn Learning instructor whose courses have reached more than 25 million people across 100+ countries. After beginning his career at Andersen Consulting and Ernst & Young, he earned a PhD in organizational behavior at Texas A&M and spent a decade as an award-winning professor before going solo. He is a five-time TEDx speaker and the author of Show Your Ink. In the episode, Richie and Todd explore why fear quietly limits careers, treating failure as data rather than a verdict, the people skills that outlast raw IQ, learnable self-awareness, authenticity at work, using AI without losing your voice, getting better at speaking and writing, building habits, escaping the success trap, and much more. 0:00 Intro 1:18 The fears holding your career back 2:37 Why failure is data, not a verdict 7:27 The career skills that beat raw IQ 10:19 Training your self-awareness 12:43 Overrated skills: networking & over-specialization 17:22 Being authentic at work 21:11 Which skills are AI-proof? 24:08 Getting better at public speaking 28:50 Improving your written communication 33:30 The success trap 38:11 "We're family" & other corporate lies 39:47 Final advice & who to follow Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Todd's LinkedIn newsletter — https://www.linkedin.com/in/drdewett/ Todd Dewett on LinkedIn Learning — https://www.linkedin.com/learning/instructors/todd-dewett Free LinkedIn Learning via your public library — https://www.linkedin.com/learning Gemma Leigh Roberts — https://www.linkedin.com/in/gemmaleighroberts/ Erin Shrimpton — https://ie.linkedin.com/in/erinshrimpton Connect with Todd — https://www.linkedin.com/in/drdewett/ AI-Native Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Have a Machine Learning Career in 2026 with Marina Wyss — https://www.datacamp.com/podcast/how-to-have-a-data-science-career-in-2026 New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Session Resources: https://bit.ly/4fk3NoX Register for this session: https://www.datacamp.com/webinars/create-an-sql-performance-improvement-agent Slow, expensive SQL queries are a persistent drain on data teams—but with the right approach, most performance problems are fixable. As organisations run increasingly complex queries across large enterprise databases, the cost of inefficient SQL compounds quickly. AI agents are now changing how analysts and engineers tackle this problem, making it faster than ever to identify bottlenecks and apply proven performance principles. In this code-along webinar, Kevin Kline, Senior Staff Technical Marketing Manager at SolarWinds, will walk you through how to use AI agents to diagnose and improve SQL query performance. You'll work through real-world case studies, explore the principles of high-performance SQL, and learn how to navigate the complexity of enterprise database environments. Whether you're writing queries daily or managing database infrastructure, you'll leave with practical techniques you can apply immediately.

Writing a credible market entry strategy report used to demand days of manual research, competitive analysis, and synthesis work. Agentic AI tools like Perplexity Computer are changing that calculus — enabling practitioners to automate multi-step research workflows while maintaining the quality control standards that business decisions demand. Knowing how to direct, verify, and iterate on AI-generated research is quickly becoming a core skill for data and AI professionals. In this code-along webinar, Michał Krzyżanowski, Head of Data & AI at Kubo, will show you how to use Perplexity Computer to build an end-to-end workflow that produces a structured Market Entry business strategy report. You will follow along as Michał configures and runs automated research tasks, applies quality control checkpoints to validate AI outputs, and assembles a polished deliverable — leaving you equipped to adapt the same approach to your own business strategy or research use cases.

AI-powered research tools are reshaping how business leaders gather intelligence, evaluate markets, and make strategic decisions — but getting reliable, high-quality output requires more than a simple prompt. Knowing how to frame queries, layer context, and critically assess what AI returns is quickly becoming a core professional skill for anyone working at the intersection of strategy and technology. In this code-along webinar, Nancy Yaklich, Head of Innovation & Incubation at Caterpillar, will show you how to use Perplexity as a deep research engine for real business problems. You'll learn how to engineer context effectively to steer AI research toward sharper, more relevant results, how to evaluate the quality and reliability of AI-generated output, and how to apply these techniques to a hands-on use case spanning competitor analysis, strategic scenario building, and business planning.

Teaching with DataCamp Classroom just got smarter. In this session, discover how DataCamp's new MCP (Model Context Protocol) connects your classroom to AI assistants like Claude — so you can pull learner analytics in plain English, automate repetitive setup, and build custom workflows that fit how you actually teach. We'll start with a quick intro to what MCP is and why it matters for educators, then show it in action. Leave with practical ways to spend less time managing and more time teaching.

Built on Anthropic's 4D AI Fluency Framework and developed with academic experts Prof. Joseph Feller (University College Cork) and Prof. Rick Dakan (Ringling College), this session gives faculty a practical model for bringing AI fluency into their own teaching practice. We'll walk through how to apply the framework to real course design, learning outcomes, assignments, and materials — so you leave with concrete ways to adapt it to your discipline, not just theory.

Meet your students where they already work. In this hands-on session, we'll show you how to integrate DataCamp Classrooms directly into your Canvas or Moodle setup, bringing interactive data and AI courses, exercises, and assessments into your existing workflow. You'll leave knowing exactly how to assign, track, and grade — no extra platforms for students to juggle.

What's actually happening with AI on campus? We'll share fresh 2026 findings on how institutions, instructors, and students are adopting AI — the trends, the surprises, and what it means for the way you teach next semester.

Helen Wall — Founder of Helen Data Design & Microsoft Influencer — on how Power BI has evolved across five years of updates, why AI features are finally surfacing, and the thing that still trips teams up: documenting your data and choosing the right fields, not all of them. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=GSpPJL8Vnz4 Connect with Reynold Xin on LinkedIn: https://www.linkedin.com/in/helenrmwall/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business #datacamp #dataframed #podcast

Top students aren't just working harder — they're using AI to study smarter. In this practical walkthrough, we'll show how NotebookLM turns readings, lecture notes, and sources into an interactive study partner that surfaces insights and reinforces understanding. Bring these techniques back to your students to help them learn more effectively.

A single AI assistant that can check your calendar, triage your inbox, send a message and pull the right file—all in one run—is no longer science fiction. But getting one that's genuinely useful (and safe to let loose on your accounts) takes some deliberate design. In this hands-on code-along, Dan Denney, Senior Software Engineer at DataCamp, walks you through building your own AI productivity assistant from scratch. You'll design the workflows that let one agent handle several everyday tasks across the tools you already use, connect it to those tools step by step, and set sensible guardrails so it only touches what you want it to.

Learning, built for you. Before a lesson begins, DataCamp's AI tutor asks who you are, what you do, and where you're going. From that moment, everything is built around you. The world's #1 AI tutor for AI and data upskilling is waiting. Every AI and data skill. Every level. Any scale. Start your journey :point_right: https://www.datacamp.com/learn/ai-tutor What makes the AI tutor different: Built for you. Not everyone. Every explanation, example, and piece of feedback is built around what you do. Not tailored or bolted on. Built from scratch, every time. Stuck? It slows down. Ahead? It speeds up. The AI tutor responds to every answer—not just whether it's right, but why, and what comes next. That kind of responsiveness is what makes a great teacher. Always current. Never outdated. In AI and data, six months can make a course misleading. The AI tutor reflects what's used in production today and evolves as the field moves — so you're always learning what actually matters. Master skills in a fraction of the time. Not because the course is harder. Because the learning experience adjusts to the work you do and the skills you actually need to master. Try it for free, only on DataCamp: https://www.datacamp.com/learn/ai-tutor

Helen Wall — Founder of Helen Data Design & Microsoft Influencer — explains why a semantic layer is the key to consistent, scalable analytics: build the model once, and your whole team can reuse it without rewriting SQL every time. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=GSpPJL8Vnz4 Connect with Reynold Xin on LinkedIn: https://www.linkedin.com/in/helenrmwall/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business #datacamp #dataframed #podcast

Session Resources: https://bit.ly/44Gw8Q5 Register for this session: https://www.datacamp.com/webinars/how-to-do-agentic-data-analysis The boundaries of what AI can do in data analysis are shifting fast — and so is the confusion about where automation helps and where it falls short. In this code-along, Dave Wentzel, Technical Evangelist at Microsoft's Office of the CTO, walks you through agentic data analysis in practice: the tech stack powering agentic analytics, how to "think meta" for sharper AI-assisted problem solving, and what these shifts mean for data analysis roles and careers. Ideal for analysts and scientists ready to move beyond experimenting with AI to integrating it into their daily workflows.

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design. In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Helen: https://www.linkedin.com/in/helenrmwall/ Microsoft AI for Good Lab: https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/ Power BI monthly feature updates: https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-latest-update SQL Server Analysis Services: https://learn.microsoft.com/en-us/analysis-services/analysis-services-overview Power BI Q&A visual: https://learn.microsoft.com/en-us/power-bi/create-reports/power-bi-tutorial-q-and-a DAX: https://learn.microsoft.com/en-us/dax/ AI-Native Course: Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: The Data Team's Agentic Future - https://www.datacamp.com/podcast/the-data-teams-agentic-future New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Cloning a production database has always been slow, expensive, and risky enough to take production down. In this clip, Databricks co-founder and Chief Architect Reynold Xin (co-creator of Apache Spark) breaks down Lakebase branching: a copy-on-write clone of your entire database in under a second, that auto-scales to zero, runs your whole CI/CD pipeline on every pull request, and costs roughly a penny before it's thrown away. It's a genuinely new way to build — ephemeral databases that don't exist until you need them, with perfect isolation from production. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=US6Z_buKizk Connect with Reynold Xin on LinkedIn: https://www.linkedin.com/in/rxin/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business #datacamp #dataframed #podcast

For 40 years, databases have been split into two worlds — transactional and analytical — and the brittle pipelines shuttling data between them are why data engineers get paged at 3am. In this clip, Databricks co-founder and Chief Architect Reynold Xin (co-creator of Apache Spark) explains why he jokes that CDC doesn't stand for "change data capture" but "continuous data corruption," and how Databricks' new LTAP approach makes every transactional table show up in the lakehouse in Iceberg format — always fresh, with no pipeline to build. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=US6Z_buKizk Connect with Reynold Xin on LinkedIn: https://www.linkedin.com/in/rxin/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries? Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto. In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Reynold: https://www.linkedin.com/in/rxin Genie: https://www.databricks.com/product/genie Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents LTAP: https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse Lakebase: https://www.databricks.com/product/lakebase Apache Spark: https://spark.apache.org AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Session Resources (including slides, security kit + repo): https://bit.ly/44ZYVPB Register for this session: https://www.datacamp.com/webinars/claude-skills-for-leaders Tomorrow’s session—AI for Optimizing Customer Lifetime Value: https://www.datacamp.com/webinars/ai-for-optimizing-customer-lifetime-value Claude Skills are transforming how enterprise leaders delegate work to AI — but most organizations are still figuring out how to adopt them effectively, govern them safely, and build them for real executive use cases. In this code-along, Tom Nassr (CEO at XRAY) and Mark Campos (Cofounder at XRAY) walk you through adopting Claude Skills in an enterprise setting: building Skills for executive use cases, governing a Skills library at scale, and the privacy and security considerations to address before deploying AI Skills in production.

Session Resources: https://bit.ly/4po9aa4 Register for this session: https://www.datacamp.com/webinars/create-claude-skills-for-marketing Register for Wednesday’s session: https://www.datacamp.com/webinars/ai-for-optimizing-customer-lifetime-value Claude Skills let you automate repetitive tasks and build reusable AI workflows. In this code-along, Rhys Phillips (Marketing Manager at DataCamp) walks you through creating Claude Skills from scratch with a marketing lens — the prompt and context engineering that make Skills genuinely useful, with hands-on builds for real marketing use cases you can apply right away.

For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries? Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto. In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Reynold: https://www.linkedin.com/in/rxin Genie: https://www.databricks.com/product/genie Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents LTAP: https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse Lakebase: https://www.databricks.com/product/lakebase Apache Spark: https://spark.apache.org AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Nine of the world's biggest AI labs were just graded on safety — and not one scored above a C+. The Future of Life Institute's Summer 2026 AI Safety Index ranks nine developers across six domains, from risk assessment to governance. Anthropic, OpenAI, and Google DeepMind lead, but the whole industry sits at mediocre-or-failing: xAI, DeepSeek, and Mistral all earned failing grades across three continents, and Mistral debuted dead last at 0.33 despite the EU's regulatory leadership — a paradox researchers call 'European dissonance.' The panel found safety commitments weakening under competitive pressure, with labs reversing bans on military work to chase defense contracts, even as capabilities race ahead. We also cover OpenAI's GPT-5.6 family (Sol, Terra, and Luna) plus GPT-Live, Meta's Muse Spark 1.1 with its new Muse Image and Muse Video media models, and SpaceXAI's Grok 4.5, co-trained with Cursor. AI-Native Course this week This week's recommended course is Understanding the EU AI Act — the perfect companion to this week's safety-and-governance story, walking through how the world's most comprehensive AI regulation actually classifies and controls AI risk. https://www.datacamp.com/courses/understanding-the-eu-ai-act About DataCamp The Median is DataCamp's weekly AI newsletter. DataCamp helps millions of learners build real, AI-native data and AI skills. DataCamp Mobile: https://www.datacamp.com/mobile DataCamp for Business: https://www.datacamp.com/business

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Dan: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 Lean theorem prover: https://lean-lang.org/ AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Build AI Your Users Can Trust with David Colwell - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Dan: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 Lean theorem prover: https://lean-lang.org/ AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Build AI Your Users Can Trust with David Colwell - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Most teams have no idea how often their AI is actually wrong. Dan Klein explains why hallucinations are like an iceberg — the ones you catch are a fraction of the real rate — why an enterprise's true hallucination rate is often 5x what they believe, and how to actually measure and monitor it before and after you ship. Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group. He previously co-founded Semantic Machines (acquired by Microsoft in 2018) and built APT, a model designed from the ground up for reliable agentic AI. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=nPRn1o0VK58 Connect with Dan Klein: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Dan: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 Lean theorem prover: https://lean-lang.org/ AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Build AI Your Users Can Trust with David Colwell - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

When an AI agent keeps making mistakes, the instinct is to add a safety net — put a human in the loop, have a second AI check the first, or wrap the model in guardrails. Dan Klein explains why each of these popular fixes tends to fall short, and why real reliability has to be built into the model rather than bolted on afterward. Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group. He previously co-founded Semantic Machines (acquired by Microsoft in 2018) and built APT, a model designed from the ground up for reliable agentic AI. 🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=nPRn1o0VK58 Connect with Dan Klein: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Learn data and AI skills with DataCamp → https://www.datacamp.com New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Dan: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 Lean theorem prover: https://lean-lang.org/ AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Build AI Your Users Can Trust with David Colwell - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Autonomous research agents are transforming how teams gather, synthesize, and act on information, but building them reliably requires more than wiring up an LLM. Getting agent memory, tool orchestration, and enterprise governance right is the difference between a prototype and a system you can trust in production. For AI engineers, Manus and Oracle together offer a powerful foundation for building agents that are both capable and production-ready. In this code-along webinar, Casius Sibanda Lee, AI Developer Advocate at Oracle, will show you how to design and build an autonomous research agent using Manus and Oracle's infrastructure. You'll work through an end-to-end agentic workflow, explore strategies for managing agent memory and architecture, and learn how to apply enterprise governance guardrails so your agent operates safely and reliably at scale.

Helping customers find the right product is one of retail's oldest and highest-leverage challenges — and one where AI agents can have immediate impact. Traditional search and filtering tools rely on exact keyword matches and predefined categories, leaving shoppers frustrated when they can't articulate precisely what they want. AI agents that understand intent, ask clarifying questions, and reason across a product catalogue can fundamentally change the discovery experience, increasing conversion and reducing abandonment. In this code-along webinar, Ivan Bykanov, Product Lead at Lightspeed Commerce, will show you how to build a product discovery agent from scratch. You'll learn how to structure and prepare product data so agents can reason over it effectively, design an agent that interprets shopper intent, and deploy a working tool that guides customers to the right product.

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Connect with Dan: https://www.linkedin.com/in/dan-klein/ Scaled Cognition: https://www.scaledcognition.com/ Berkeley NLP Group: https://nlp.cs.berkeley.edu/ Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 Lean theorem prover: https://lean-lang.org/ AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: How to Build AI Your Users Can Trust with David Colwell - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe. In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: • Virtual Lab (Nature paper) — https://www.nature.com/articles/s41586-025-09442-9 • Einstein Arena — https://einsteinarena.com/ • DS Gym — https://github.com/fannie1208/DSGym • Paper2Agent — https://github.com/jmiao24/Paper2Agent • Together AI — https://www.together.ai/ • AlphaFold 2 / Nobel Prize 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/ • Connect with James — https://www.linkedin.com/in/james-zou-2123a4133 • AI-Native Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov — https://www.datacamp.com/podcast/how-ai-agents-will-work-while-you-sleep New to DataCamp? Learn on the go using the DataCamp mobile app — https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business — https://www.datacamp.com/business

Session Resources (GitHub Repo + Set-Up Info): https://bit.ly/44aqEgi Register for this session: https://www.datacamp.com/webinars/build-a-banking-workflow-app-using-django-mongodb-and-voyage-ai Automating complex office workflows is one of the most impactful ways AI developers can deliver real business value—and the banking sector is full of high-stakes, document-heavy processes ripe for transformation. By combining a lightweight web framework with vector search and retrieval augmented generation, developers can build intelligent workflow tools that go well beyond simple CRUD apps. In this code-along webinar, Afi Gbadago, Senior Developer Advocate at MongoDB, will show you how to build a banking workflow application from the ground up using Django, MongoDB, and Voyage AI. You'll walk through creating a functional web app in Django, wiring up semantic search with MongoDB Atlas Vector Search, and adding RAG capabilities powered by Voyage AI embeddings to automate document-heavy banking tasks. This session is ideal for AI developers looking to apply LLM-backed retrieval to real-world enterprise use cases.

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe. In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: • Virtual Lab (Nature paper) — https://www.nature.com/articles/s41586-025-09442-9 • Einstein Arena — https://einsteinarena.com/ • DS Gym — https://github.com/fannie1208/DSGym • Paper2Agent — https://github.com/jmiao24/Paper2Agent • Together AI — https://www.together.ai/ • AlphaFold 2 / Nobel Prize 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/ • Connect with James — https://www.linkedin.com/in/james-zou-2123a4133 • AI-Native Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov — https://www.datacamp.com/podcast/how-ai-agents-will-work-while-you-sleep New to DataCamp? Learn on the go using the DataCamp mobile app — https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business — https://www.datacamp.com/business

Repetitive office tasks — drafting reports, sifting through data, and chasing down information across disconnected tools — consume hours that teams could spend on higher-value work. Amazon Quick, AWS's AI assistant for work, changes that equation by connecting to your tools (Slack, Teams, CRMs, databases, and documents) and automating routine workflows, all within a secure, permission-aware environment. For AI practitioners and business professionals, knowing how to put these capabilities to work is fast becoming an essential skill. In this code-along webinar, Sowjanya Pandruju and Dilip Karandikar, Senior Cloud Architect and Senior Practice Leader at AWS, will show you how to harness Amazon Quick to automate routine office tasks end to end. You'll learn how Amazon Quick integrates safely into enterprise environments — respecting existing access controls and compliance requirements — before walking through a practical use case: analysing sales data and generating a polished report with minimal manual effort.

AI personal assistants are proliferating rapidly, but most implementations expose serious security gaps — running agents with broad system access, no credential isolation, and no meaningful sandboxing. For AI engineers building or deploying these systems, security and governance are not optional extras; they are the difference between an assistant that can be trusted with real workflows and one that cannot be put into production. In this code-along webinar, Amit Shafnir, Founding AI Engineer at NanoCo, will show you how to build a secure AI professional assistant using NanoClaw: the open source, container-isolated framework built on frontier agent SDKs. You'll get started with an out-of-the-box agent template and customize it for a real business use case. Along the way, he'll demonstrate how NanoCo helps teams build agents with built-in security and governance controls, so they're ready for real-world use.

Building useful AI agents has historically required either a heavyweight framework or significant custom plumbing. Hermes Agent, the open-source agentic framework from Nous Research, changes that — giving AI engineers a persistent, self-improving foundation for building agents that learn, remember, and reuse skills across tasks. Whether you're automating workflows or building intelligent assistants, Hermes offers a practical and powerful starting point. In this code-along webinar, Nihal Kumar Kadri, AI Engineer at Infosys, will walk you through how to build a shopping assistant using Hermes. You'll explore how to architect effective agents, leverage Hermes's persistent memory and skill system, and apply the framework to a real-world e-commerce automation use case. By the end of the session, you'll have a working shopping agent and a solid foundation for building your own Hermes-powered workflows.

Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh). In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Winning with AI: https://winningwithaibook.com/ Dr. Katia Walsh: https://www.linkedin.com/in/katiawalsh/ Moderna: https://www.modernatx.com/ Konecta: https://www.konecta.com/ IKEA: https://www.ikea.com/ Andrej Karpathy's LLM Wiki: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f Connect with Charlene: https://www.linkedin.com/in/charleneli/ AI-Native Course - Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: https://www.datacamp.com/podcast/our-data-trends-and-predictions-for-2026 New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Session Resources: https://bit.ly/4eWNPQd Register for this session: https://www.datacamp.com/webinars/multimodal-deep-learning-for-credit-scoring Credit scoring is evolving beyond traditional tabular data. By incorporating multiple data modalities—such as transaction history, text data, and alternative signals—machine learning models can deliver more accurate and nuanced assessments of risk. This hands-on session will show you how to build modern credit scoring systems using multimodal approaches. In this code-along webinar, María Óskarsdóttir, a Professor at the University of Southampton, will guide you through building and analyzing a credit scoring model in Python. You'll explore how to combine different types of data, design models that leverage multimodal inputs, and evaluate performance in a real-world financial context. This session is ideal for ML practitioners looking to push beyond standard modeling techniques.

Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh). In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Winning with AI: https://winningwithaibook.com/ Dr. Katia Walsh: https://www.linkedin.com/in/katiawalsh/ Moderna: https://www.modernatx.com/ Konecta: https://www.konecta.com/ IKEA: https://www.ikea.com/ Andrej Karpathy's LLM Wiki: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f Connect with Charlene: https://www.linkedin.com/in/charleneli/ AI-Native Course - Intro to AI for Work: https://www.datacamp.com/courses/introduction-to-ai-for-work Related Episode: https://www.datacamp.com/podcast/our-data-trends-and-predictions-for-2026 New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Session Resources (set-up info inside!): https://bit.ly/4438PQ4 Register for this session: https://www.datacamp.com/webinars/build-a-no-code-ai-banking-agent AI agents are transforming financial services—from automating customer support to assisting with compliance and decision-making. But building agents in a regulated environment requires careful design, strong controls, and an understanding of where no-code tools can safely accelerate development. In this code-along webinar, Anjali Jain, an Enterprise AI Architect at Metro Bank, will guide you through building a no-code AI banking agent. You'll explore real-world use cases in financial services, learn how to design agents that perform reliably, and understand how to operate within the constraints of a regulated industry. This session is ideal for practitioners looking to combine speed with responsibility.

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe. In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: • Virtual Lab (Nature paper) — https://www.nature.com/articles/s41586-025-09442-9 • Einstein Arena — https://einsteinarena.com/ • DS Gym — https://github.com/fannie1208/DSGym • Paper2Agent — https://github.com/jmiao24/Paper2Agent • Together AI — https://www.together.ai/ • AlphaFold 2 / Nobel Prize 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/ • Connect with James — https://www.linkedin.com/in/james-zou-2123a4133 • AI-Native Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov — https://www.datacamp.com/podcast/how-ai-agents-will-work-while-you-sleep New to DataCamp? Learn on the go using the DataCamp mobile app — https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business — https://www.datacamp.com/business