In this 90-minute workshop, machine learning engineers and data scientists will learn practical techniques for identifying and mitigating age bias in AI-driven hiring systems. We’ll explore fairness metrics like statistical parity, counterfactual fairness, and equalized odds, and demonstrate how tools such as Fairlearn, Aequitas, and AI Fairness 360 can be used to monitor and improve model fairness. Through hands-on exercises, participants will walk away with the skills to evaluate and de-bias models in high-risk areas like recruitment.

This hands-on workshop covers how to use open source ML models like LSTMs and TimeSeries LLM's, with Python to try to forecast weather patterns, with best practices for data preparation and real time predictions.

Data professionals often excel at technical skills but struggle to connect their work to broader business goals. In this show, we're joined by Jordan Morrow, Senior VP of Data and AI Transformation at AgileOne and author of Business 101 for the Data Professional, to discuss bridging the gap between data and strategy. Jordan offers practical advice on developing business acumen, aligning data work with organizational goals, and communicating effectively with stakeholders. Whether you're a data scientist, analyst, or aspiring leader, this conversation will help you unlock the full potential of your data career. What You'll Learn: The essential business skills every data professional needs How to align data projects with organizational goals Strategies for effective communication with non-technical stakeholders Common pitfalls data professionals face when navigating business challenges—and how to avoid them Actionable tips to become a more strategic, business-savvy data professional   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sinch⁠ — Connect with customers at every step of their journey. •⁠ Modal⁠ — The cloud platform for building AI applications. — How has Microsoft changed since its founding in 1975, especially in how it builds tools for developers? In this episode of The Pragmatic Engineer, I sit down with Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft. Scott has been with the company for 28 years. He built the first prototype of ASP.NET, led the Windows Phone team, led up Azure, and helped shape many of Microsoft’s most important developer platforms. We talk about Microsoft’s journey from building early dev tools to becoming a top cloud provider—and how it actively worked to win back and grow its developer base. In this episode, we cover: • Microsoft’s early years building developer tools  • Why Visual Basic faced resistance from devs back in the day: even though it simplified development at the time • How .NET helped bring a new generation of server-side developers into Microsoft’s ecosystem • Why Windows Phone didn’t succeed  • The 90s Microsoft dev stack: docs, debuggers, and more • How Microsoft Azure went from being the #7 cloud provider to the #2 spot today • Why Microsoft created VS Code • How VS Code and open source led to the acquisition of GitHub • What Scott’s excited about in the future of developer tools and AI • And much more! — Timestamps (00:00) Intro (02:25) Microsoft’s early years building developer tools (06:15) How Microsoft’s developer tools helped Windows succeed (08:00) Microsoft’s first tools were built to allow less technically savvy people to build things (11:00) A case for embracing the technology that’s coming (14:11) Why Microsoft built Visual Studio and .NET (19:54) Steve Ballmer’s speech about .NET (22:04) The origins of C# and Anders Hejlsberg’s impact on Microsoft  (25:29) The 90’s Microsoft stack, including documentation, debuggers, and more (30:17) How productivity has changed over the past 10 years  (32:50) Why Gergely was a fan of Windows Phone—and Scott’s thoughts on why it didn’t last (36:43) Lessons from working on (and fixing)  Azure under Satya Nadella  (42:50) Codeplex and the acquisition of GitHub (48:52) 2014: Three bold projects to win the hearts of developers (55:40) What Scott’s excited about in new developer tools and cloud computing  (59:50) Why Scott thinks AI will enhance productivity but create more engineering jobs — The Pragmatic Engineer deepdives relevant for this episode: • Microsoft is dogfooding AI dev tools’ future • Microsoft’s developer tools roots • Why are Cloud Development Environments spiking in popularity, now? • Engineering career paths at Big Tech and scaleups • How Linux is built with Greg Kroah-Hartman — 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].

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Opening keynote featuring a leader from AWS Startups discussing the intersection of AWS startups and AI.

Keynotes & panels featuring industry leaders from Google, AWS, IBM, PayPal, Bank of America, Capital One, Visa, JPMorgan Chase, Hyatt Hotels Corporation, United Nations, Fireworks AI, WaveAI, EA, Dropbox, Ancestry, Oracle, LinkedIn, and more.

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 🎧 REPLAY ALERT: The Future of Risk, Powered by AI In case you missed it the first time — we’re bringing back this incredible conversation with Kathy Stares, EVP of North America at Provenir. Kathy dives into how Provenir’s AI-powered risk decisioning software processes over 4 billion transactions annually across 50+ countries. From fintech to traditional banking, learn how this platform is shaking up risk strategies with automation, innovation, and global reach. 🌍 Why does risk decisioning need AI?  🚀 What’s driving the next wave of banking transformation?  📊 How can Gen Z shape the future of financial services? Timestamps:  01:14 Meet Kathy Stares 04:34 The Start of Summer 08:01 AI-Powered Risk Decisioning 11:31 The Tech Behind Provenir 13:42 Models 14:32 Data Source 16:43 The Business Problem 17:33 Banking Strategies 18:48 Platform Scope 20:28 Automation 21:46 Provenir’s Differentiator 22:47 Millennials & Gen Z 29:05 Trends to Watch 31:57 Kathy’s Takeaway 33:28 Connect with Provenir 35:25 The Wealthy Barber🎙️ Guest: Kathy Stares 🔗 Learn more: provenir.com

AIinFinance #FintechInnovation #RiskDecisioning #FinancialServices #AIReplay #Automation #FutureOfBanking #GenZFinance #WomenInTech #MakingDataSimple #TechPodcast #ProvenirAI #PodcastReplay

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.

In this episode, we dive into a genetic mystery: how can a single gene in plant-parasitic nematodes have thousands of alleles? This study unravels the bizarre behaviour of HYP effectors — genes that help nematodes infect plants but defy traditional genetics.

Using CRISPR, long-read sequencing, and clever maths, the researchers reveal:

​How the HYP gene rearranges motifs in its hyper-variable domain (HVD) with flawless precision​That most nematodes appear homozygous, despite the population showing extreme diversity​A proposed mechanism called HVD editing — a form of locus-specific somatic genome editing​Why this mirrors the way our immune system reshuffles antibody genes

This isn’t just about plant pests — it’s a rare glimpse at real-time genome innovation, where diversity is generated with intent, not random chance.

📖 Based on the research article: “A gene with a thousand alleles: The hyper-variable effectors of plant-parasitic nematodes” Unnati Sonawala, Helen Beasley, Peter Thorpe, Kyriakos Varypatakis, Beatrice Senatori, John T. Jones, Lida Derevnina & Sebastian Eves-van den Akker Published in Cell Genomics (2024). 🔗 https://doi.org/10.1016/j.xgen.2024.100580

🎧 Subscribe to the WoRM Podcast for more strange and spectacular tales of genome biology.

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 moving faster than ever. AI techniques should bring adaptability to an uncertain world in constant flux. However, despite its extraordinary power and early promises, AI has not been leveraged to its full potential. What is missing? Where did we go wrong? Join us as we discuss our ambition for the future of AI and AI should do for us to deliver the value that we are expecting.

Small Language Models (SLMs) provide a viable alternative to Large Language Models (LLMs) for developing high-performing, cost-effective, and secure generative AI solutions. CDAOs, AI architects, and data architects should attend this presentation to gain insights into the strengths and weaknesses of SLMs and discover five specific use cases where SLMs outperform LLMs.

Try Julius.ai 👉 https://bit.ly/4jn4cFF Coupon code: AVERY25 AI is transforming how we work, how we make decisions, and how we understand the world through data. In this episode, I explore how Julius AI can simplify your data tasks, automate repetitive work, and offer valuable insights in MINUTES. Dive into the future of data analysis and get ready to 10x your productivity! Get my weekly newsletters (free): https://www.datacareerjumpstart.com/newsletter

Learn more about my CRM Course Creator 360: https://coursecreatorpro.com/registeremailaffiliate?am_id=avery8756

How This Delivery Driver Became a FAANG Data Analyst (Jen Hawkins) https://youtu.be/f-BWp_IJZ-I?si=2_tKqHEng_EYNRCB

💌 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 01:55 - My CRM Data Analysis 02:23 Exploring and Cleaning Data with Julius AI 06:15 Email Analysis and Insights 13:39 Sales Cycle Length Analysis 15:27 The Power of AI in Data Analysis

🔗 CONNECT WITH MY TOP NEWSLETTER ENGAGERS! Isaac Oresanya: https://www.linkedin.com/in/isaac-oresanya/ Jen Hawkins: https://www.linkedin.com/in/jeandriska/ David Mills: https://www.linkedin.com/in/david-mills/ Mukta Pandey: https://www.linkedin.com/in/mukta-pandey-30a89b243/

🔗 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

AI agents are emerging as an important trend since they enable levels of business adaptability, flexibility and agility that traditional AI systems can’t achieve. This flexibility is valuable in unpredictable environments where real-time monitoring and control aren’t practical. Autonomous behaviors, despite significant societal, legal and ethical implications, are the answer to the increasing complexity in our enterprise systems.

Analytics is experiencing another monumental change. Just as visual drag and drop BI tools and augmented insights led to changes in analytics delivery, we now experience conversational interfaces, automated workflows and AI agents that cause us to rethink how analytics will be done. Join this session to learn the new technologies that are making an impact and how this will affect plans for future investment in analytics tools, platforms and solutions.

In a fragmented data landscape, reactive processes trap enterprise teams in firefighting mode, hindering innovation and scalability.Acceldata’s Agentic Data Management introduces a new paradigm, embedding proactive, AI-driven autonomy and cross-domain intelligence into data operations. By eliminating bottlenecks and reducing manual burden, it accelerates trustworthy insights and scalable governance. Join Acceldata to discover how forward-thinking enterprises are modernizing their data strategies to power innovation—and why autonomous operations are essential for thriving in an AI-first world.

Legacy systems slow organisations due to scale limits, risk, and cost. In this session, we will walkthrough how enterprises use Archon Data Store, our AI-powered archival platform, to create enterprise-wide data archival strategies for their legacy and modern data. Through use cases from finance, pharma and manufacturing - we will learn intelligent archival techniques that enable organisations to discover their data; align their past, present and future data footprint through decommissioning and migration while enhancing system performance, security and stewardship for their enterprise data.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.