Session led by Philipp Schmid, AI Developer Experience at Google DeepMind

Session led by Sai Kumar Arava, Senior Machine Learning Manager, Gen AI / ML Applications at Adobe

Strategic adoption of Generative AI: use cases and benefits for organizations.

Foundational technical concepts of Generative AI explained in accessible terms.

Session led by Valentina Alto, Technical Architect, AI & App at Microsoft

Join us for an insightful session led by Joao, CEO of CrewAI, as he explores how enterprises are putting agentic AI to work in real-world environments. As organizations move beyond experimentation and into full-scale deployment, they’re encountering both immense opportunity and critical operational challenges. In this session, Joao will unpack how leading companies are building and deploying agentic workflows in production—showcasing how autonomous agents are being used to handle complex tasks, coordinate across systems, and deliver tangible business outcomes. He will highlight the practical considerations enterprises must address to succeed at scale, including governance frameworks that ensure agents behave reliably and transparently, and observability techniques that allow teams to monitor, debug, and optimize these dynamic systems in real time. Drawing from real production use cases, Joao will share how organizations across industries—from telecom and financial services to retail and logistics—are leveraging CrewAI’s platform to orchestrate agentic workflows that drive efficiency, enhance decision-making, and improve customer experiences.

João Moura, CEO of CrewAI, leads this session on how enterprises are deploying agentic AI in real-world environments—from experimentation to full-scale production. The talk covers building and deploying agentic workflows, how autonomous agents coordinate across systems to deliver business outcomes, governance frameworks for reliable and transparent agent behavior, and observability techniques to monitor and debug dynamic systems in real time. Drawing from production use cases across industries, the session explores how CrewAI’s platform orchestrates agentic workflows to drive efficiency and improve customer experiences.

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

An expert talk exploring how AI agents are being built, deployed, and scaled across industries.

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: Inscrições do Data Hackers Challenge 2025 Live de Bain: Estratégias de GenAI para análise de dados não-estruturados Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Send us a text The AI Advantage: Get Better Results from LLMs with the Perfect Prompt On this episode of Making Data Simple, we’re joined by Jonathan Mast, AI consultant and coach at Whitebeard Strategies and creator of the Perfect Prompting Framework™. Jonathan’s not just riding the AI wave—he’s teaching business leaders and everyday users how to surf it, with simple, actionable tools that unlock meaningful results from large language models. If you've ever stared at a prompt box wondering what to type—or worse, gotten garbage back from AI—this episode is for you. We talk about what works, what doesn’t, and what’s coming next (agents, anyone?). Plus, Jonathan breaks down his 4-step framework that’s helping 300K+ community members and clients scale AI with clarity and confidence. ⏱️ Episode Timestamps 01:34 Introducing Jonathan Mast04:13 Digital Agency05:29 Whitebeard Strategies08:06 ADD09:57 Back to Whitebeard14:51 The Perfect Prompting Framework21:36 The Four Step Method24:58 What if You Don't Use AI?28:37 Agents30:08 Whitebeard Engagements32:42 Getting Started36:39 What's True But Not a Consensus?37:23 For Fun🔗 Connect with Jonathan LinkedIn: https://www.linkedin.com/in/jonathanjmast/Website: https://whitebeardstrategies.com#MakingDataSimple #PerfectPromptingFramework #AIforBusiness #AIProductivity #JonathanMast #PromptEngineering #LLMs #AIAgents #WhitebeardStrategies #TechPodcast #DataSimplified Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Valerie Vegera, Senior UX Researcher at Grammarly, invites you to discover how AI is evolving from a novel curiosity to an indispensable partner in people's daily workflows. The talk explores designing AI experiences grounded in real human needs and how to integrate them into the ways people work.

Sonal Malhotra, User Research Lead at Klarna, shares her journey of using AI in qualitative user research: what’s worked, what hasn’t, and where the real value lies. She discusses benefits like faster analysis and surfaced themes, alongside challenges such as loss of nuance and over-reliance on automation.

The data analysis landscape is changing rapidly. New AI tools are emerging every week, and it can sometimes feel overwhelming. So in this video, I compare ChatGPT and Julius AI to see how they stack up against each other. We'll use a dataset of 1,444 data job listings from FindADataJob.com to analyze trends in the 2025 data job market to answer the question: Which AI tool is best suited for your data analysis needs? Where I Go To Find Datasets (as a data analyst) 👉 https://datacareerpodcast.com/episode/131-7-resources-to-find-amazing-datasets-free 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 Introduction 00:20 Comparing ChatGPT and Julius AI 01:17 Uploading and Previewing Data 03:01 Data Analysis Suggestions 05:04 What State Has The Most Jobs Listed? 08:08 Analyzing Job Trends Over Time 10:31 Customizable Chart Themes 11:10 Analyzing Job Salary Trends 17:01 Investigating Experience Levels in Job Listings 19:44 Handling Missing Data with MissingNo 21:34 Exploring Interesting Trends in the Dataset 25:34 Conclusion: Which Tool Should You Use? 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

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Summary In this episode of the Data Engineering Podcast Kacper Łukawski from Qdrant about integrating MCP servers with vector databases to process unstructured data. Kacper shares his experience in data engineering, from building big data pipelines in the automotive industry to leveraging large language models (LLMs) for transforming unstructured datasets into valuable assets. He discusses the challenges of building data pipelines for unstructured data and how vector databases facilitate semantic search and retrieval-augmented generation (RAG) applications. Kacper delves into the intricacies of vector storage and search, including metadata and contextual elements, and explores the evolution of vector engines beyond RAG to applications like semantic search and anomaly detection. The conversation covers the role of Model Context Protocol (MCP) servers in simplifying data integration and retrieval processes, highlighting the need for experimentation and evaluation when adopting LLMs, and offering practical advice on optimizing vector search costs and fine-tuning embedding models for improved search quality.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Kacper Łukawski about how MCP servers can be paired with vector databases to streamline processing of unstructured dataInterview IntroductionHow did you get involved in the area of data management?LLMs are enabling the derivation of useful data assets from unstructured sources. What are the challenges that teams face in building the pipelines to support that work?How has the role of vector engines grown or evolved in the past ~2 years as LLMs have gained broader adoption?Beyond its role as a store of context for agents, RAG, etc. what other applications are common for vector databaes?In the ecosystem of vector engines, what are the distinctive elements of Qdrant?How has the MCP specification simplified the work of processing unstructured data?Can you describe the toolchain and workflow involved in building a data pipeline that leverages an MCP for generating embeddings?helping data engineers gain confidence in non-deterministic workflowsbringing application/ML/data teams into collaboration for determining the impact of e.g. chunking strategies, embedding model selection, etc.What are the most interesting, innovative, or unexpected ways that you have seen MCP and Qdrant used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector use cases?When is MCP and/or Qdrant the wrong choice?What do you have planned for the future of MCP with Qdrant?Contact Info LinkedInTwitter/XPersonal websiteParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links QdrantKafkaApache OoziNamed Entity RecognitionGraphRAGpgvectorElasticsearchApache LuceneOpenSearchBM25Semantic SearchMCP == Model Context ProtocolAnthropic Contextualized ChunkingCohereThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this episode, host Kirk Offel sits down with Andy Davis for an insightful and candid conversation straight from the heart of the DCAC event in Ireland. Together, they pull back the curtain on the rapidly changing world of data centers—a sector Kirk compares to the new backbone of civilization, shaping everything from the way we stream our favorite shows to the meteoric rise of AI.

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