Data Neighbor Podcast

• Ep40: Why Most AI Agents Fail - And How to Build Agents You Can Count On

  AI is moving fast, but reliable agents are still rare. In this Data Neighbor Podcast, we sit down with Jigyasa Grover, ML Engineer at Uber, author of Sculpting Data for ML: The first act of Machine Learning, and member of Google’s ML Advisory Board, to unpack why most AI agents fail and what it really takes to build ones you can count on.Jigyasa shares how to design, evaluate, and secure reliable agent systems - from memory management and adversarial testing to using human judgment without slowing down innovation.Connect with the team (tell us YouTube sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Jigyasa: https://www.linkedin.com/in/jigyasa-grover/In this episode, Jigyasa explains how agents evolve beyond simple workflows into autonomous systems, why evals are at the heart of reliable AI, and how developers can prevent silent failures through better design, testing, and observability.You'll learn about:-Why most AI agents fail and how to engineer reliability from day one-Workflow agents vs LLM-based agents-How evals, memory hygiene, and adversarial testing improve reliability-When to use traditional ML instead of LLMs-Designing for human judgment, security, and recovery in agent systems#aipodcast #aiagents #aidevelopment #aiengineering #llm #mlops #datascience #agentdesign #workflowagents #memory #evaluation #productstrategy #aiproductmanagement #autonomousagents #aiethics #aideployments #reliableai #dataneighbor #jigyasagrover #agenticai

  --------  

  53:03

  --------

  53:03
• Ep39: How to 10X Data Work with HEX Agentic AI

  AI is reshaping data and analytics, moving from brittle dashboards to agentic, conversational workflows. In this Data Neighbor Podcast, we sit down with Barry McCardel, CEO & Co-founder of Hex, to unpack how agentic analytics, natural-language querying, and semantic modeling are changing how data teams (and the whole business) make decisions. Connect with Shane, Sravya, and Hai (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Barry: https://www.linkedin.com/in/barrymccardel/In this episode, Barry shares how Hex evolved beyond notebooks into a self-serve BI + AI agent platform, why PMF is a moving target in AI, and how great data teams are shifting from ticket queues to curation, governance, and partnership.You'll learn about:- Agentic analytics in practice: from “chat with my data” to explainable, reproducible workflows (thinking traces, SQL visibility, versioned projects).- How semantic models (Hex, Snowflake, dbt, Cube) unlock trusted self-serve BI.- How to find PMF in AI: sustaining product-market fit when model capabilities shift weekly.- What is Data team 2.0: moving repetitive “pull a number” requests to agents so humans focus on curation, modeling, experimentation, and strategy.- How to ship rigor at speed: why transparency, lineage, and observability matter for trust—not just accuracy.#aiproductmanagement #agenticanalytics #conversationalbi #datateams #selfserveBI #semanticlayer #dbt #snowflake #dataapps #llm #aiagents #mlops #productstrategy #dataneighbor #hextech #hex #datascience #ai

  --------  

  42:10

  --------

  42:10
• Ep38: How to Land a Machine Learning Job Today

  Is the future of Machine Learning Engineer (MLE) jobs secure in the age of AI? Umang Chaudhary, an ML Engineer at TikTok (formerly Amazon), dives deep into this pressing question and shares his invaluable insights on navigating the rapidly evolving ML landscape. In this episode, Umang recounts his unique journey from web development to a thriving MLE career, the challenges of ML interview prep, and why he's now dedicated to guiding aspiring ML professionals.Discover how Umang leverages cutting-edge AI tools like Gemini and Grok in his daily workflow and for interview preparation, offering a fresh perspective on productivity and learning. Learn about the common fears and questions his mentees face regarding AI's impact on job security and how to differentiate between "real-world" ML skills and those needed to ace interviews. This episode is a must-watch for anyone looking to break into or advance in the ML field, offering a blend of career guidance, practical tips, and a compelling look into the future of AI.In this episode, you will learn:* The evolving role of AI and LLMs in daily ML workflows, from solution building to enhanced productivity.* How Umang leverages AI tools like Gemini Pro and Grok for efficient coding, document analysis, and comprehensive ML system design interview preparation.* Umang's unique journey, transitioning from web development to a Machine Learning Engineer role at Amazon and then TikTok.* Current concerns from aspiring ML professionals about AI's impact on the future of MLE jobs and Umang's perspective on career longevity.* Inspiring stories of individuals making unconventional transitions into ML engineering roles, including web developers, data analysts, and product managers.* A four-step plan to effectively break down and master Machine Learning interview preparation (ML fundamentals, ML design, ML system design, ML coding).* The critical importance of patience and a strategic "numbers game" approach to landing an ML job in today's competitive market.Connect with Umang:https://www.linkedin.com/in/mlwithumang/https://www.instagram.com/umangabroad/https://www.instagram.com/ml.with.umang/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#MLEngineer #MachineLearning #AIJobs #LLM #AICareers #MLCareerGuidance #MLInterviewPrep #TikTok #Amazon #DataScience #TechCareers #CareerTransition #Grok #ChatGPT #Gemini #Entrepreneurship #MachineLearningEngineer #AIInnovation #DataNeighborPodcast

  --------  

  36:13

  --------

  36:13
• Ep37: The Boring Future of Data Engineering (And Why It's a Good Thing)

  Unlock the secrets to building a future-proof data organization that thrives on impact, not just effort. Join us as we sit down with Manoj Mohan, former Engineering Leader of Data and AI Platforms at Intuit, and a seasoned leader from Meta, Cloudera, and Apple. Manoj shares his deep insights from two decades in the data, ML, and AI space, offering pragmatic strategies for long-term success.In this episode, you’ll discover:- Hard-won lessons from early data warehouse failures and the critical role of humility and scalability in data projects.- Why embracing a "platform as a product" mindset for data engineering is essential for long-term efficiency and avoiding KPI chaos.- Manoj Mohan's powerful "3 Gs" framework (Grounded, Guarded, Governed) for deploying Large Language Models (LLMs) responsibly and effectively within the enterprise, comparing them to high-speed Formula One cars that need robust guardrails.- A visionary outlook on what a future-proof data organization might look like by 2030, where AI-driven insights are seamlessly accessible to every employee.- Practical advice for startups on balancing speed with sustainable data infrastructure, ensuring foundational blocks are built alongside product innovation.- Key principles for data leaders, including the importance of continuous learning, unlearning, and focusing on problem-solving over tools.Whether you're a data engineer, an AI enthusiast, a data leader, or navigating data challenges in a startup, this episode is packed with invaluable wisdom to help you build resilient, scalable, and impactful data systems.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataEngineering #AIPlatforms #LLMs #DataStrategy #Scalability #DataGovernance #ResponsibleAI #PlatformAsAProduct #FutureOfData #DataOrganization #StartupData #EnterpriseAI #DataLeadership #MLEngineering #DataManagement #ManojMohan #DataNeighborPodcast #TechLeadership

  --------  

  57:19

  --------

  57:19
• Ep36: How Top AI Product Managers Evaluate Products

  AI is changing product management, from how teams prototype to how they measure success. In this episode of the Data Neighbor Podcast, we’re joined by Aman Khan, Head of Product at Arize AI (LLM evaluation & observability). Aman breaks down the three emerging AI PM archetypes (AI-native PM, AI platform PM, and AI-powered PM), how to move from “vibe coding” to eval-driven development (EDD), and why aligning evals to business outcomes matters more than any single accuracy score. He also shares hard-won tactics for handling subjectivity in LLM outputs, setting user expectations in UX, and deciding when rigor can (and can’t) slow down speed. In this episode, you’ll learn:-The three ways AI shows up in PM work—and how those roles are converging.-A practical ladder from “vibe checks” to EDD (evals in dev & production), including LLM-as-a-judge and when to trust it.-How to tie evals to business metrics (trust, value, speed) and resolve “good eval, bad outcome” conflicts.-UX patterns for long-running agent tasks (progress, ETAs, checkpoints) that preserve trust.-Where AI coding tools help most (and least) across engineers, PMs, and data teams.Connect with Aman Khan:LinkedIn: https://www.linkedin.com/in/amanberkeley/🌐 Website: https://amank.ai🏢 Arize AI: https://arize.com/ Arize AIConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r#aiproductmanagement #aievals #llmobservability #productmanagement #datascience #mlops #aiagents #evaluation #productstrategy #dataneighbor #arizeai #llms

  --------  

  55:34

  --------

  55:34

Mais podcasts de Tecnologia

Podcasts em tendência em Tecnologia

Sobre Data Neighbor Podcast