Data Neighbor Podcast

• Ep21: From Zero to GenAI - What ML Engineers & Data Scientists Need to Learn

  How is AI quietly reshaping your everyday workflow—whether you notice it or not? In this episode of the Data Neighbor Podcast, we explore how AI is being embedded directly into the tools you already use, with real-world examples from Adobe Acrobat.Our guest, Nikhil Pentapalli, is a Senior Machine Learning Engineer at Adobe, where he works on embedding GenAI into Acrobat to transform how people interact with documents. From AI assistants that can summarize and answer questions across multiple PDFs, to intelligent restructuring of scanned documents for mobile, we unpack what it really looks like to bring AI into production—and why it’s harder than it seems.We cover:- How AI is becoming invisible infrastructure in everyday workflows. - Why integrating AI into documents is harder than you'd think. - What makes PDFs complex, and how GenAI is making them smarter. - Building GenAI products: from prompt engineering to memory optimization. - Advice for breaking into GenAI, and what ML engineers need to know now. - Traditional ML vs LLMs: where to use which, and how to manage costs.Whether you're building AI tools, thinking about career moves, or just curious about what it takes to ship real GenAI products, this episode is loaded with insights and practical advice.Connect with Hai, Sravya, and Shane:- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#genai #aiworkflow #documentai #rag #retrievalaugmentedgeneration #acrobatai #llm #promptengineering #mlengineer #aiintegration #realworldai #aiproducts #datascience #dataneighborpodcast

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• Ep20: How to Build a Data Analytics App in Minutes (Replit, Cursor, Bolt, V0, Lovable)

  AI is transforming how products are built—and with new AI prototyping tools like Replit, Cursor, V0, Bolt, and Lovable, it’s never been easier to go from idea to working app in minutes. In this episode of the Data Neighbor Podcast, we’re joined by Colin Matthews, founder of Tech for Product, to explore how product managers, data scientists, and engineers can build faster using AI.Colin teaches two of the top AI and technical fluency courses on Maven, writes the Tech for Product Substack, and helps builders leverage tools like Replit and GPT to build and test real products—fast.📚 Courses from Colin:AI Prototyping for Product Managers: https://maven.com/tech-for-product/ai-prototyping-for-product-managersTechnical Foundations for Product Managers: https://maven.com/tech-for-product/tech-fundamentals💼 Follow Colin Matthews:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/Substack: https://blog.techforproduct.com/Website: https://techforproduct.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feIn this episode, we cover:- How to prototype real apps with AI (Replit, Cursor, Lovable, Bolt, V0)- How AI is changing the role of product managers and engineers- Replit demo: How we built a working data analysis app with zero code- AI vs traditional product development: What’s faster?- When these tools are actually production-ready- How product teams can use AI to validate ideas early- AI tools for data science, analytics, and product strategy- Best practices for debugging and building with AI agents- The real role of product-market fit in AI-first prototyping- How to avoid common mistakes when using AI tools- Colin’s advice for staying up to date with the fast-moving AI stackIf you work in AI, product, data science, machine learning, or tech strategy—or you’re a founder trying to get your idea off the ground—this episode is a goldmine of practical insights. From MVPs to experimentation platforms, we explore how AI is changing who gets to build, how fast they can do it, and what the future of product development looks like.#AI #AIPrototyping #ProductManagement #ProductDevelopment #DataScience #MachineLearning #AIApps #Replit #Cursor #V0 #Bolt #Lovable #AIForStartups #NoCodeAI #GPTApps #TechForProduct #MavenCourses #AICodingTools #BuildWithAI #AIProductStrategy #AIforPMs #RapidPrototyping #TechnicalFluency #LLMTools #AIStack #AIEngineering #DataTools #AIUX #Streamlit #OpenAI #AIAppDemo

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• Ep19: 4 Data-Driven Techniques to Secure Your Next Job in a Tough Market

  Are you struggling to land a data science role in today’s competitive market? In this episode, we sit down with Karun Thankachan, Senior Data Scientist at Walmart and former Amazon Applied Scientist, to break down the science of job searching - including actionable strategies to optimize your job applications, referrals, and LinkedIn outreach. Oh and we covered how recommendation systems are built and what all go into them in practice.What You’ll Learn in This Episode:- The metrics-driven approach to job searching: How many applications, referrals, and LinkedIn connections should you aim for?- How AI is changing hiring for both recruiters and candidates- The importance of networking and why it’s the most underrated skill in data science- A deep dive into recommender systems—how AI powers Netflix, Amazon, and Walmart recommendations- How LLMs (Large Language Models) are revolutionizing recommendation enginesIf you’re an aspiring data scientist, a mid-career professional looking for new opportunities, or just interested in how AI is reshaping hiring and recommender systems, this episode is packed with insights you don’t want to miss!Connect with Karun, Hai, Sravya, and Shane:Karun: https://www.linkedin.com/in/karunt/Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe#DataScience #JobSearch #AIHiring #RecommenderSystems #MachineLearning #NetworkingTips #CareerGrowth #AI #Walmart #Amazon #DataNeighborPodcast #TechHiring

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• Ep18: Open-Source LLMs vs. ChatGPT: Which One Should You Use?

  AI is evolving faster than ever—and open-source AI models are catching up to proprietary models at an incredible pace. In this episode of the Data Neighbor Podcast, we sit down with Maarten Grootendorst, co-author of Hands-On Large Language Models with Jay Alammar, DeepLearning.AI instructor, and creator of BERTopic and KeyBERT, to break down the real differences between open-source and closed-source AI models.We’ll discuss how LLMs (Large Language Models) evolved from bag-of-words and Word2Vec to modern transformer-based models like BERT, GPT-4, DeepSeek, LLaMA 2, and Mixtral. More importantly, we explore when open-source AI models might actually be better than proprietary models from OpenAI, Google DeepMind, and Anthropic.Hands-On Large Language Models (Maarten’s Book): https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961DeepLearning.AI Course: How Transformer LLMs Work: https://www.deeplearning.ai/short-courses/how-transformer-llms-work/Maarten’s AI Newsletter: https://newsletter.maartengrootendorst.com/Connect with us!Maarten Grootendorst: https://www.linkedin.com/in/mgrootendorst/Hai Guan: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya Madipalli: https://www.linkedin.com/in/sravyamadipalli/Shane Butler: https://www.linkedin.com/in/shaneausleybutler/

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• Ep17: How to Use AI and Build Data Science Teams

  In this episode of the Data Neighbor Podcast, we sit down with Justin Chen, Senior Director of Growth Data Science and former Head of Data Science & Engineering at Coinbase, to dive deep into how to build scalable, high-impact data teams. We get into the mind of a data leader who has built numerous reputable data organizations to understand principles, lessons, and challenges every company faces. Justin shares invaluable insights on:- Go slow to go fast – Why early-stage speed can create long-term inefficiencies.- The evolution of data science – How different orgs (growth, core, platform) function within companies.- The myth of the unicorn data scientist – Why hiring for specialization is key.- AI's impact on data science – How automation and AI tools are shaping the future of analytics.- Getting a seat at the table – How data professionals can move from support roles to strategic leadership.Justin also shares his firsthand experience of building an AI-powered data copilot at Coinbase to streamline analytics workflows, offering a sneak peek into how AI will shape the next generation of data teams. Whether you’re a data scientist, engineer, or aspiring leader, this conversation is packed with practical advice and industry wisdom you won’t want to miss!Connect with Justin: https://www.linkedin.com/in/mingc/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe#DataScience #AI #aiengineering #TechLeadership #MachineLearning #GrowthDataScience #CareerAdvice #Analytics #Hiring #DataEngineering #DataDriven #DataTeams #TechPodcast #aiagents

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