As AI-powered agents and workflows grow in complexity, understanding their internal behavior becomes critical. In this hands-on workshop, you’ll build an agent and explore how observability tooling in PyCharm can help you trace, inspect, and debug its behavior at every stage – without having to leave the IDE.

In this episode, I sit down with Ole to discuss his new book, "Fundamentals of Metadata Management." We move past the simple definition of "data about data" to a more nuanced view of metadata as something that exists in two places at once , serving as a pointer to find information elsewhere. Ole introduces his core concept of the "MetaGrid"—the interconnected, yet siloed, web of metadata repositories that already exists within every large organization across various teams and technologies. He argues that the key to better metadata management is not to build a new monolithic system but to recognize, document, and integrate the MetaGrid that's already there, hiding in plain sight. The conversation also covers the impact of the AI hype cycle , the lessons learned from the Data Mesh movement , the sociological incentives that help or hinder metadata projects , and the cultural clash between the worlds of data engineering and library science.

A workshop session to show you the basics on how to use Docling to enhance document ingestion in your AI workflow.

Send us a text This week on Making Data Simple, join Ajay Kulkarni, CEO and co-founder of TigerData, as we dive into the rapidly evolving world of data. Ajay shares his front-row perspective on the challenges and opportunities of building and scaling time-series databases in an era of AI-driven automation. From the mechanics of managing massive data streams to the bold bets shaping the future of IoT, this conversation goes deep into what’s breaking, what’s working, and what’s next. Whether you’re a data engineer, tech leader, or simply fascinated by the speed of AI innovation, this episode is packed with insights you won’t want to miss. 01:15 Meet AJ Kulkarni04:29 TigerData07:16 Timeseries 09:25 Use Cases 11:03 Why Progress? 11:58 Why TigerData16:05 AI is Everything21:06 The Fastest Postgres 25:45 Advanced Features28:53 Future of IOT36:48 The Future of TigerData38:03 San Francisco38:26 A Big Bet41:06 Good BooksLinkedIn: https://www.linkedin.com/in/ajaykulkarni/ Website:  https://tigerdata.com 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.

Keynote talk by Maria Börner, Head of the AI Competence Center at Westernacher Solutions.

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

Is a wiring diagram enough to understand the brain? In this episode, we dive into how researchers combined whole-brain optogenetic stimulation with calcium imaging in C. elegans to reveal functional neural connections that go beyond the traditional connectome.

Key insights include:

A new functional atlas built from ~23,000 neuron pair experiments How neuropeptides and extrasynaptic signals contribute to brain activity Strong functional links often exist without anatomical connections A data-driven rethinking of how neural signals propagate and integrate Implications for plasticity, brain evolution, and full-organism modelling

This episode sheds light on how small brains can perform complex processing — by rewiring our assumptions about wiring.

📖 Based on the research article: “Neural signal propagation atlas of Caenorhabditis elegans” Francesco Randi, Anuj K. Sharma, Sophie Dvali & Andrew M. Leifer Published in Nature (2023) 🔗 https://doi.org/10.1038/s41586-023-06683-4

🎧 Subscribe to the WOrM Podcast for more full-organism insights into behaviour, neuroscience, and beyond.

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

Abiel Reinhart joins Nora Szentivanyi to discuss the recent surge in tech-related business investment, its impact on US growth, and what it might mean for productivity gains. Tech investment accounted for about a third of US GDP growth––and much of the expansion in domestic final sales––in 1H25. While hyperscaler capex levels are expected to stay high in coming years, current growth rates are unlikely to be sustained, implying a smaller GDP contribution in 2026. We also discuss potential mismeasurement of tech investment in the GDP accounts. 

This podcast was recorded on September 23, 2025. 

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved.

A session exploring the cutting-edge realm of Agentic AI. Hari Prasad Renganathan will present a custom outline focused on advanced applications and techniques with LLMs.

Presentation of the brand new State of AI in Platform Engineering industry report drawn from a survey of over 200 platform engineers. Topics include: how the intersection of AI and platform engineering is driving the AI revolution; is “prompt fatigue” the new cognitive overload; and the gap between AI potential and realized value.

Presentation of the brand new State of AI in Platform Engineering industry report, based on a survey of over 200 platform engineers. Sam Barlien will discuss: how AI and platform engineering intersect to drive AI-driven transformation; is 'prompt fatigue' the new cognitive overload; and the disconnect between AI potential and realized value.

It’s now over six years since the emergence of the paper "How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh” by Zhamak Dehghani that had a major impact on the data and analytics industry. 

It highlighted major data architecture failures and called for a rethink in data architecture and in data provisioning by creating a data supply chain and democratising data engineering to enable business domain-oriented creation of reusable data products to make data products available as self-governing services. 

Since then, we have seen many companies adopt Data Mesh strategies, and the repositioning of some software products as well as the emergence of new ones to emphasize democratisation. But is what has happened since totally addressing the problems that Data Mesh was intending to solve? And what new problems are arising as organizations try to make data safely available to AI projects at machine-scale?  

In this unmissable session Big Data LDN Chair Mike Ferguson sits down with Zhamak Dehghani to talk about what has happened since Data Mesh emerged. It will look at:

● The drivers behind Data Mesh

● Revisiting Data Mesh to clarify on what a data product is and what Data Mesh is intending to solve

● Did data architecture really change or are companies still using existing architecture to implement this?

● What about technology to support this - Is Data Fabric the answer or best of breed tools? 

● How critical is organisation to successful Data Mesh implementation

● Roadblocks in the way of success e.g., lack of metadata standards

● How does Data Mesh impact AI?

● What’s next on the horizon?

Today, we’re joined by Thomas Scott, CEO of Wrike, an intelligent work management platform where anyone can build, connect, automate, and scale workflows. We talk about:

How personalization is accelerating the pace of software adoptionCo-creating the future with early adoptersMeeting customers where they are, because AI is not monolithicIdentifying & understanding information gaps in order to deliver value to customersSetting a North Star for your role in the industry & staying focused on solving customer pains

AI agents are everywhere in the headlines — but what do they really do? In this episode of Data & AI with Mukundan, we cut through the hype and explain, in plain language, how AI agents actually think. You’ll learn the three key ingredients that separate real agents from chatbots: Planning: breaking big goals into concrete stepsMemory: remembering context so actions feel consistentFeedback: adjusting when things go wrong instead of collapsingI also walk through a live demo where an AI agent triages my Google Calendar and Gmail inbox. You’ll hear exactly how it proposes deep-work sessions, admin blocks, and even a workout — all while avoiding conflicts and asking for my approval before making changes. To make this practical, I include a true/false quiz to test your instincts and a set of discussion prompts to help you imagine how agents could save you hours in real life. By the end of this episode, you’ll know: How to tell the difference between a chatbot, automation, and a real AI agentWhy most flashy demos online fail in practiceHow to build your first mini-agent with just one tool and a simple feedback loopWhy agents only matter if they give you back time and focusIf you’ve ever wished for an assistant to handle your messy inbox, your cluttered schedule, or your repetitive tasks, this episode will show you how AI agents can actually do it. No buzzwords, no sci-fi — just practical, useful AI that you can build today. Links & Resources Today's Demo: HereQuiz: HereRecording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)Join the Newsletter: Free Email Newsletter to receive practical AI tools weekly.Join the Discussion (comments hub): https://mukundansankar.substack.com/notes🔗 Connect with Me:Website: Data & AI with MukundanTwitter/X: @sankarmukund475LinkedIn: Mukundan SankarYouTube: Subscribe

Data storytelling matters more than ever. If you have the ability to make your analysis understood—and acted on—it can make you more valuable than analysts with twice your experience. In this episode, Mike Cisneros walks us through his practical, tactical playbook to turn good analysis into powerful data stories that get results. ✨ Try Julius today at https://landadatajob.com/Julius-YT

Make your data storytelling sing. Check out Mike's co-authored book here: Storytelling with Data: Before and After - Practical Makeovers for Powerful Data Stories Amazon link: https://amzn.to/41ViFmv Website: storytellingwithdata.com/books

💌 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 01:16 How To Become A Better Data Analyst and Storyteller 04:41 Storytelling with Data: Before and After 15:33 A Case Study: Analyzing Call Center Data

🔗 CONNECT WITH MIKE 🎥 YouTube Channel: https://www.youtube.com/c/storytellingwithdata 🤝 LinkedIn: https://www.linkedin.com/in/mikevizneros/ https://www.linkedin.com/company/storytelling-with-data-llc/ 📸 Instagram: https://www.instagram.com/mikevizneros/ 💻 Website: https://www.storytellingwithdata.com/

🔗 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

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

In this interactive workshop, Sunil Soares will break down the key learning points from the morning's keynote session, exploring real-world embedded AI applications, and unpack how regulations like the EU AI Act impact autonomous systems. You’ll leave with practical tools and explore how to govern AI responsibly—without slowing innovation.

AI agents have seen increasing adoption across multiple industries as the next wave of AI. Existing regulations such as the European Union AI Act also apply to AI agents.

The session is based on Sunil Soares' books that can be found here: https://yourdataconnect.com

This session will cover the following topics:

• Agentic AI governance framework

• Applications with embedded AI

• Associated AI Governance Regulations including EU AI Act

• Agentic AI Governance Platforms

In today’s fast-moving global business environment, technological innovation, shifting consumer demands, and the growing imperative of sustainability are redefining how organizations operate and compete. To thrive in this dynamic landscape, businesses must place data at the core of their strategic and operational decisions, embracing digital tools, AI, and automation to unlock productivity and resilience.

This engaging presentation offers a powerful framework for building a tailored data strategy aligned with organizational goals. Led by an expert with over two decades of strategic leadership experience across Fortune 500 companies, FTSE 100 firms, non-profits, and SMEs, this session combines real-world insights with actionable tools to help you navigate complexity and drive sustained growth.

Whether you’re developing a data strategy from the ground up or enhancing an existing approach, this session will empower you to lead with clarity, drive innovation, and future-proof your business.

Key Takeaways:

• Emerging Trends: Discover how to integrate disruptive technologies, including AI, into your data strategy.

• Innovation Blueprints: Learn to create a culture of creativity, confidence, and capability.

• Strategic Foundations: Explore the essential building blocks of a successful data strategy.

• Global Perspectives: Benefit from international case studies of both triumphs and lessons learned.

• Interactive Q&A: Bring your challenges to the table and gain expert advice and fresh perspectives.

If you’re serious about making smarter decisions, driving growth, and staying ahead of the curve — this session is for you.

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS) (NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translation AI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysis Cybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network security Social good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops. This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently. Understand the distinct features of foundation model-enabled AI agents Discover the core components and design principles of AI agents Explore design trade-offs and implement effective multiagent systems Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field