A pesquisa State of Data Brazil 2025, conduzida pelo Data Hackers em parceria com a Bain & Company, reuniu mais de 5,2 mil profissionais de dados para entender os desafios, tendências e transformações do setor. Esse é o maior mapeamento já realizado sobre o mercado brasileiro de trabalho em dados e inteligência artificial !! Neste episódio, recebemos Felipe Fiamozzini (Expert Partner na Bain & Company) para explorar os principais insights do relatório, como: Salários e evolução das carreiras em dados e IA; Tendências tecnológicas e adoção de GenAI; Impacto dos layoffs e mudanças no modelo de trabalho; e o que esperar do mercado de dados em 2025. Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Falamos no episódio Felipe Fiamozzini — Expert Partner na Bain & Company Nossa Bancada Data Hackers: Monique Femme — Head of Community Management na Data HackersGabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart. Referências: Semana de tecnologia Itaú: https://comunicatech.itau.com.br/semanadatecnologia2025_datahackersBaixe a pesquisa State of Data Brazil 2025: https://www.datahackers.news/p/relatorio2024-2025

Time Series Analysis with Spark provides a practical introduction to leveraging Apache Spark and Databricks for time series analysis. You'll learn to prepare, model, and deploy robust and scalable time series solutions for real-world applications. From data preparation to advanced generative AI techniques, this guide prepares you to excel in big data analytics. What this Book will help me do Understand the core concepts and architectures of Apache Spark for time series analysis. Learn to clean, organize, and prepare time series data for big data environments. Gain expertise in choosing, building, and training various time series models tailored to specific projects. Master techniques to scale your models in production using Spark and Databricks. Explore the integration of advanced technologies such as generative AI to enhance predictions and derive insights. Author(s) Yoni Ramaswami, a Senior Solutions Architect at Databricks, has extensive experience in data engineering and AI solutions. With a focus on creating innovative big data and AI strategies across industries, Yoni authored this book to empower professionals to efficiently handle time series data. Yoni's approachable style ensures that both foundational concepts and advanced techniques are accessible to readers. Who is it for? This book is ideal for data engineers, machine learning engineers, data scientists, and analysts interested in enhancing their expertise in time series analysis using Apache Spark and Databricks. Whether you're new to time series or looking to refine your skills, you'll find both foundational insights and advanced practices explained clearly. A basic understanding of Spark is helpful but not required.

As data professionals, mastering the technical aspects of AI and data is only half the battle. The real challenge lies in effectively communicating insights to drive action and influence decisions. How do you ensure your data stories resonate with diverse audiences? It's not just about the numbers—it's about crafting a narrative that speaks to stakeholders. What strategies can you employ to make your insights not only heard but impactful? Abhijit Bhaduri advises organizations on talent and leadership development. As the former Partner and GM Global L&D of Microsoft, Abhijit led their onboarding and skilling strategy especially for people managers. Forbes described him as "the most interesting generalist from India." The San Francisco Examiner described him as the "world’s foremost expert on talent and development" and among the ten most sought-after brand evangelists. Abhijit also teaches in the Doctoral Program for Chief Learning Officers at the University of Pennsylvania. Prior to being at Microsoft, he led an advisory practice helping organizations build their leadership, talent and culture strategy. His latest book is called "Career 3.0 – Six Skills You Must Have To Succeed." In the episode, Richie and Abhijit explore the complexities of modern career paths, the importance of experimentation and adaptability, the evolution of career models from 1.0 to 3.0, the impact of longevity on career strategies, essential skills for career advancement, and much more. Links Mentioned in the Show: Abhijit’s newsletter on Linkedin - Dreamers and Unicorns Abhijit’s Book - Career 3.0 – Six Skills You Must Have To SucceedConnect with AbhijitSkill Track: AI FundamentalsRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Vaibhav Gupta and I chat about our shared love for tacos in Austin, the evolution of AI tools, and why new thinking and approaches are required for AI-first tools.

BAML: https://github.com/BoundaryML/baml

Supported by Our Partners • Graphite — The AI developer productivity platform.  • Sonar —  Code quality and code security for ALL code.  • Chronosphere — The observability platform built for control. — How do you take a new product idea, and turn it into a successful product? Figma Slides started as a hackathon project a year and a half ago – and today it’s a full-on product, with more than 4.5M slide decks created by users. I’m joined by two founding engineers on this project: Jonathan Kaufman and Noah Finer. In our chat, Jonathan and Noah pull back the curtain on what it took to build Figma Slides. They share engineering challenges faced, interesting engineering practices utilized, and what it's like working on a product used by millions of designers worldwide. We talk about: • An overview of Figma Slides • The tech stack behind Figma Slides • Why the engineering team built grid view before single slide view • How Figma ensures that all Figma files look the same across browsers • Figma’s "vibe testing" approach • How beta testing helped experiment more • The “all flags on”, “all flags off” testing approach • Engineering crits at Figma • And much more! — Timestamps (00:00) Intro (01:45) An overview of Figma Slides and the first steps in building it (06:41) Why Figma built grid view before single slide view (10:00) The next steps of building UI after grid view  (12:10) The team structure and size of the Figma Slides team  (14:14) The tech stack behind Figma Slides (15:31) How Figma uses C++ with bindings  (17:43) The Chrome debugging extension used for C++ and WebAssembly  (21:02) An example of how Noah used the debugging tool (22:18) Challenges in building Figma Slides  (23:15) An explanation of multiplayer cursors  (26:15) Figma’s philosophy of building interconnected products—and the code behind them (28:22) An example of a different mouse behavior in Figma  (33:00) Technical challenges in developing single slide view  (35:10) Challenges faced in single-slide view while maintaining multiplayer compatibility (40:00) The types of testing used on Figma Slides (43:42) Figma’s zero bug policy  (45:30) The release process, and how engineering uses feature flags  (48:40) How Figma tests Slides with feature flags enabled and then disabled (51:35) An explanation of eng crits at Figma  (54:53) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside Figma’s engineering culture • Quality Assurance across the tech industry • Shipping to production • Design-first software engineering — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Nora Szentivanyi joins Bruce Kasman to discuss key takeaways from the latest Global Inflation Monitor and how the incoming data and tariff news are shaping our inflation views. Global goods prices are firming even before tariffs were put in place, with pressures broadening outside the US. As more tariffs are imposed, this puts the onus on still-sticky services inflation to do much of the heavy lifting in getting inflation down.  We retain our sticky global core inflation view and see upside risks to our forecast for global core inflation to moderate to below 3%ar in coming quarter. Beneath this sticky inflation perspective, we continue to see scope for greater diversity in inflation outcomes across countries.

This podcast was recorded on March 26, 2025.

This communication is provided for information purposes only.  Institutional clients can view the related report at https://www.jpmm.com/research/content/GPS-4943283-0 ,  https://www.jpmm.com/research/content/GPS-4795397-0  for more information; please visit www.jpmm.com/research/disclosures for important disclosures.

© 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

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: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Send us a text 🎙️ From Host to Hot Seat! This week on Making Data Simple, Al Martin flips the mic and becomes the guest as Ian Smith, co-founder of Lighthouse Technology, steps in to host a fast-paced, thought-provoking episode of the Baseline Podcast. It’s a casual convo with some serious zingers—think startup energy, AI honesty, automation truths, and even a little NFL shoutout. You won’t want to miss this one! 🕒 Episode Highlights 00:52 – How Making Data Simple got started 06:11 – IBM as a Startup?! 08:38 – Why IBM, really 11:49 – Competing with “Real-time” AI 23:20 – How to get clients to truly embrace automation 32:12 – The test AI flunked… but still got a B 🤖 33:06 – GO CHIEFS! 🏈 37:54 – The real IBM use case 47:06 – “Nothing went wrong” (famous last words?) 🔗 Connect with Ian: linkedin.com/in/ian-smith-a803701 🌐 Learn more: baselinepodcast.com 💡 Want to be featured on Making Data Simple? Pitch us your story at [email protected]—we’re always on the lookout for brilliant minds in tech, data, leadership, and innovation. 🎧 Hosted by Al Martin, WW VP Technical Sales, IBM. LinkedIn. Executive Producer, Katherine Mayne LinkedIn. We talk trending tech, bold ideas, and big leadership—all with a focus on keeping it simple and fun. 

MakingDataSimple #BaselinePodcast #TechLeadership #AIConversations #StartupTalk #AutomationInnovation #IBMTech #DataDriven #PodcastGuest #BusinessInnovation #LighthouseTechnology #AlMartin #IanSmith #GoChiefs

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.

Can a normally bacteria-feeding nematode become a blood-feeder? In this episode, we dive into the surprising world of haematophagic Caenorhabditis elegans — worms that can consume human blood. This research explores how feeding C. elegans a diet of erythrocytes (red blood cells) could help accelerate vaccine development for parasitic infections like hookworm disease.

By studying the enzymes used by these worms to digest haemoglobin and detoxify haem, scientists are unlocking new ways to test anti-parasite vaccines — all without needing live hookworms.

🔍 Key Topics Covered: • How C. elegans can ingest and survive on a diet of human blood • Using fluorescently labelled red blood cells to track feeding behaviour • Comparing digestive enzymes of C. elegans to those of Necator americanus, a major human parasite • Why this breakthrough could help identify and test new vaccine candidates

📖 Based on the research article: “Haematophagic Caenorhabditis elegans” Veeren M. Chauhan & David I. Pritchard. Published in Parasitology (2019). 🔗 Read it here: https://doi.org/10.1017/S0031182018001518

Join us to discover how turning a free-living nematode into a blood-feeder could reshape vaccine research for parasitic diseases!

🎧 Subscribe to the WoRM Podcast for more surprising stories at the intersection of parasitology, biotechnology, and innovation.

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]

AI is revolutionizing industries, but many organizations struggle to translate their AI investments into measurable business value. In this session, Eva Dong, Lead of AI Monetization at Google Cloud, will demystify AI monetization and provide a practical framework for unlocking value while optimizing costs.

Mark Freeman joins me to chat about data contracts, the crazy life of being the first employee at a hot startup, writing books and creating content, and much more.

DataEngineering #Startups #AI #DataQuality #DataContracts

I talk with job search expert Steve Dalton about his radical approach to landing your dream job-- WITHOUT applying online! As the author of 'The Job Closer' and 'The 2-Hour Job Search, Steve advocates for a networking-based strategy and explains the importance of asking for advice rather than referrals. 💌 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 - How to Become a Data Analyst w/o Applying 1000 Jobs 00:00 - Introduction 02:18 - Steps to effective job searching 05:06 - The 2-Hour Job Search 10:54 - Asking strangers for advice vs. applying online 18:35 - Earned referrals vs. online referrals 20:24 - PremiumDataJobs.com and DataFairy.io 24:37 - Effective outreach messages 27:18 - The Role of AI in Job Searching 28:16 - The 6-Point Email 34:00 - Ed Bernier's "Three-Hour Rule" 38:57 - Advice for job seekers

🔗 CONNECT WITH STEVE 🤝 LinkedIn: https://www.linkedin.com/in/daltonsteve/ 📸 Instagram: https://www.instagram.com/dalton_steve/ 🎵 X: https://x.com/dalton_steve 💻 Website: https://2hourjobsearch.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

Summary In this episode, Mukundan Sankar discusses the challenges of content promotion and how it often overshadows the joy of content creation. He shares his journey of building an AI content engine that automates the promotion process, allowing creators to focus on their passion for writing. The conversation emphasizes the importance of reclaiming creativity through automation and offers practical insights for content creators looking to enhance their reach without the burnout of manual promotion. takeaways Content creation is often overshadowed by the burden of promotion.Each platform requires a unique approach to content sharing.Promotion can take longer than the actual writing process.Systemizing content promotion can alleviate stress for creators.Using AI tools can help automate the promotion process.Creating a custom GPT can streamline content distribution.Engagement can increase by repurposing content across platforms.Automation allows creators to focus on their passion for writing.The right tools can save time and enhance creativity.Investing in automation tools can lead to greater content impact.Automate your content so that you can focus on creating the full tutorial with prompt for each platform My Medium Blog

Summary In this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field.

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 Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of changeInterview IntroductionHow did you get involved in the area of data management?Can you describe what Ascend is and the story behind it?The last time we spoke was August of 2022. What are the most notable or interesting evolutions in your platform since then?In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities?The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams?At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability?Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center?Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on?What are the concrete impacts that these new capabilities have on teams who are using Ascend?What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend?When is Ascend the wrong choice?What do you have planned for the future of AI in Ascend?Contact Info LinkedInParting 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 AscendCursor AI Code EditorDevinGitHub CopilotOpenAI DeepResearchS3 TablesAWS GlueAWS BedrockSnowparkCo-Intelligence: Living and Working with AI by Ethan Mollick (affiliate link)OpenAI o3The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. ● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. ● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. ● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. ● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. ● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. Who this book is for: Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.

It's finally possible to bring the awesome power of Large Language Models (LLMs) to your laptop. This talk will explore how to run and leverage small, openly available LLMs to power common tasks involving data, including selecting the right models, practical use cases for running small models, and best practices for deploying small models effectively alongside databases.

Bio: Jeffrey Morgan is the founder of Ollama, an open-source tool to get up and run large language models. Prior to founding Ollama, Jeffrey founded Kitematic, which was acquired by Docker and evolved into Docker Desktop. He has previously worked at companies including Docker, Twitter, and Google.

➡️ Follow Us LinkedIn: https://www.linkedin.com/company/small-data-sf/ X/Twitter : https://twitter.com/smalldatasf Website: https://www.smalldatasf.com/

Discover how to run large language models (LLMs) locally using Ollama, the easiest way to get started with small AI models on your Mac, Windows, or Linux machine. Unlike massive cloud-based systems, small open source models are only a few gigabytes, allowing them to run incredibly fast on consumer hardware without network latency. This video explains why these local LLMs are not just scaled-down versions of larger models but powerful tools for developers, offering significant advantages in speed, data privacy, and cost-effectiveness by eliminating hidden cloud provider fees and risks.

Learn the most common use case for small models: combining them with your existing factual data to prevent hallucinations. We dive into retrieval augmented generation (RAG), a powerful technique where you augment a model's prompt with information from a local data source. See a practical demo of how to build a vector store from simple text files and connect it to a model like Gemma 2B, enabling you to query your own data using natural language for fast, accurate, and context-aware responses.

Explore the next frontier of local AI with small agents and tool calling, a new feature that empowers models to interact with external tools. This guide demonstrates how an LLM can autonomously decide to query a DuckDB database, write the correct SQL, and use the retrieved data to answer your questions. This advanced tutorial shows you how to connect small models directly to your data engineering workflows, moving beyond simple chat to create intelligent, data-driven applications.

Get started with practical applications for small models today, from building internal help desks to streamlining engineering tasks like code review. This video highlights how small and large models can work together effectively and shows that open source models are rapidly catching up to their cloud-scale counterparts. It's never been a better time for developers and data analysts to harness the power of local AI.

Risks to the global expansion are elevated in the face of a broadening trade war, but incoming news highlights a still underlying resilient expansion. The trade drag weighing down 1Q25 US growth is reflecting a boost elsewhere, particularly in Asia where the latest news points to a surge in trade activity in advance of prospective tariffs.

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 21 March 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. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker: Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. 

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background 4:00 Bartosz’s career journey from Java development to AI engineering 9:05 The importance of testing in data engineering 11:19 How to create tests for data pipelines 13:14 Tools and approaches for testing data pipelines 17:10 Choosing Spark for data engineering projects 19:05 The connection between data engineering and AI tools 21:39 Use cases of AI in data engineering and MLOps 25:13 Prompt engineering techniques and best practices 31:45 Prompt compression and caching in AI models 33:35 Thoughts on DeepSeek and open-source AI models 35:54 Using AI for lead classification and LinkedIn automation 41:04 Building Chrome extensions with AI integration 43:51 Comparing Cursor and GitHub Copilot for coding 47:11 Using ChatGPT and Perplexity for AI-assisted tasks 52:09 Hosting static websites and using AI for development 54:27 How blogging helps attract clients and share knowledge 58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz LinkedIn: https://www.linkedin.com/in/mikulskibartosz/ Github: https://github.com/mikulskibartosz Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

Nossos convidados Bárbara Correia Dos Santos, Roberto Frossard e Cadu Mazzei compartilham como o Itaú avançou na utilização de IA ao longo dos últimos anos, e como a tecnologia tem contribuído para a construção de produtos e serviços hiperpersonalizados que atendem as reais necessidades dos clientes. No episódio, você confere como as disciplinas de Responsible IA e Emerging Tech, alinhadas à uma estratégia do negócio de evolução contínua em experiência, podem transformar a forma com que as pessoas interagem com serviços financeiros. Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Falamos no episódio: Carlos Eduardo Mazzei — Diretor de Tecnologia no Itaú e líder IABárbara Correia — Coordenadora de Responsable IA em TecnologiaRoberto Frossard — Líder de Tecnologias Emergentes no ItaúNossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Monique Femme — Head of Community Management na Data Hackers Referências: Inscreva-se na Semana de Tecnologia do Itaú: https://comunicatech.itau.com.br/semanadatecnologia2025_datahackersEpisódio Computação Quântica com Samurai (Itaú): Itaú Unibanco é a primeira empresa latino-americana a filiar-se a instituto de IA de Stanford: https://epocanegocios.globo.com/inteligencia-artificial/noticia/2025/02/itau-unibanco-e-a-primeira-empresa-latino-americana-a-filiar-se-a-instituto-de-ia-de-stanford.ghtmlItaú adquire participação na startup de inteligência artificial NeoSpace: https://epocanegocios.globo.com/empresas/startup/noticia/2025/01/itau-adquire-participacao-na-startup-de-inteligencia-artificial-neospace.ghtmlInteligência Itaú: banco lança nova experiência para clientes com IA generativa: https://www.itau.com.br/relacoes-com-investidores/noticias/inteligencia-itau-banco-lanca-nova-experiencia-para-clientes-com-ia-generativa/Itaú lançará Inteligência Itaú, IA generativa para atendimento aos clientes: https://economia.uol.com.br/noticias/estadao-conteudo/2024/11/22/itau-lancara-inteligencia-itau-ia-generativa-para-atendimento-aos-clientes.htm

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. DataTopics Unpluggedis your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data—unplugged style! In this episode: OpenAI asks White House for AI regulation relief: OpenAI seeks federal-level AI policy exceptions in exchange for transparency. But is this a sign they’re losing momentum?Hot take: GPT-4.5 is a ‘nothing burger’: Is GPT-4.5 actually an upgrade, or just a well-marketed rerun?Claude 3.7 & Blowing €100 in Two Days: One of the hosts tests Claude extensively—and racks up a pricey bill. Was it worth it?OpenAI’s Deep Research: How does OpenAI’s new research tool compare to Perplexity?AI cracks superbug problem in two days: AI speeds up decades of scientific research—should we be impressed or concerned?European tech coalition demands ‘radical action’ on digital sovereignty: Big names like Airbus and Proton push for homegrown European tech.Migrating from AWS to a European cloud: A real-world case study on cutting costs by 62%—is it worth the trade-offs?Docs by the French government: A Notion alternative for open-source government collaboration.Why people hate note-taking apps: A deep dive into the frustrations with Notion, Obsidian, and alternatives.Model Context Protocol (MCP): How MCP is changing AI tool integrations—and why OpenAI isn’t on board (yet).OpenRouter.ai: The one-stop API for switching between AI models. Does it live up to the hype?OTDiamond.ai: A multi-LLM approach that picks the best model for your queries to balance cost and performance.Are you polite to AI?: Study finds most people say "please" to ChatGPT—good manners or fear of the AI uprising?AI refusing to do your work?: A hilarious case of an AI refusing to generate code because it "wants you to learn."And finally, a big announcement—DataTopics Unplugged is evolving! Stay tuned for an updated format and a fresh take on tech discussions.