The MLOps Podcast

Delving into AI in medical devices and medicine, Mila Orlovsky shares insights on practical applications, data challenges, and navigating regulatory standards. Topics include predictive analytics, business side learnings, impact of ML models in medicine, FDA compliance, future of ML and AI, and controversial predictions in the field. Essential listening for healthcare professionals looking to implement AI.

Jason Liu, an applied AI consultant and creator of Instructor, discusses challenges and applications of LLMs. Topics include making LLMs interact with existing systems, building applications with LLMs, thinking in logic and design, and the future of Instructor. They also explore misconceptions in LLMs, improving LLM applications, RAG as recommendation systems, fine-tuning embedding models, measuring impact on business outcomes, and unlocking economic value through structured data extraction.

Jinen Setpal, ML Engineer at DagsHub, discusses building, deploying, and monitoring large language models. They explore the DPT chatbot project, evaluation methods, reducing hallucinations, improving inference speed, and monitoring language models in production.

Join now to take part in our first live MLOps Podcast episode. I'll be chatting with Jinen Setpal, ML Engineer at DagsHub about his work building LLM applications and getting LLMs into production. Sign up for the event at the link here: https://www.linkedin.com/events/7098968036782596096/comments/

Yuval Gabay, MLOps Engineer at WSC Sports, discusses MLOps methodologies, standardizing deployment, and closing the loop from production to training. They also explore implementing WCS Sports in NBA games, tips for starting in MLOps, managing machine learning with GitOps, and taking ML models into production.

In this episode, I had the pleasure of speaking with Hamel Husain. Hamel is a machine learning and MLOps extraordinaire, he was one of the core maintainers of Fast.ai and has worked on ML and MLOps in places like Data Robot, Airbnb, and GitHub. We talk about Large Language Models, the future role of data scientists in the world of LLMs, and Hamel's approach to solving MLOps problems. Watch the video: https://www.youtube.com/watch?v=3oElMXPkaVs Relevant Links: 🐦 Hamel's Twitter – https://twitter.com/HamelHusain 🟦 Hamel's Linkedin – https://www.linkedin.com/in/hamelhusain/ ✍️ Hamel's amazing blog: https://hamel.dev/blog/posts/nbdev/ 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

In this episode, I had the pleasure of speaking with Almog Baku, a serial entrepreneur, consultant in Cloud, AI Infrastructure and Foundational models. We talk about Kubernetes, Large Language Models (LLMs), how to get them into production, and how data is becoming a more central piece of the ML landscape. We also Discuss Almog's newest project, Raptor ML, which helps ML teams productionize ML pipelines. Watch the video: https://www.youtube.com/watch?v=DCApRXhXD_w&feature=youtu.be Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links: 🦅 Check out Raptor for ML productionization – https://github.com/raptor-ml/raptor 👫 Join the Open AI & Gen AI TLV meetup group – https://www.meetup.com/openai-genai-tlv/ 📄 Read a very cool LLM paper – https://react-lm.github.io/ ➡️ Almog Baku on LinkedIn – https://www.linkedin.com/in/almogbaku/ ➡️ Almog Baku on Twitter – https://twitter.com/almogbaku Recommendation Links: Watch "Foundation" – https://tv.apple.com/us/show/foundation/umc.cmc.5983fipzqbicvrve6jdfep4x3 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

In this episode, I had the pleasure of speaking with Shreya Shankar, Ph.D. student at Berkeley RISELab. We chat about auto data validation and MLOps. Shreya shares her insights on several interesting topics, including the challenges of automating the data validation process and how to overcome them. We also discuss what makes organizations able to iterate faster in machine learning, and some predictions about the future of machine learning and MLOps. Watch the video: https://youtu.be/_hi6--H2Hug Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links: 📃 Moving Fast with Broken Data – https://arxiv.org/abs/2303.06094v1 🤖 Operationalizing machine learning https://arxiv.org/pdf/2209.09125.pdf 📒 Operationalizing notebooks https://smacke.net/papers/nbslicer.pdf ➡️ Shreya Shankar on LinkedIn – https://www.linkedin.com/in/shrshnk/ ➡️ Shreya Shankar on Twitter – https://twitter.com/sh_reya Recommendation Links: 📺 The Glory – A Korean Revenge Drama – https://www.netflix.com/title/81519223 ⛷️ Go Skiing! 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

In this episode, Dean speaks with Noa Weiss, the wonderful AI & ML consultant. They dive into Deep Learning research for marine mammal sounds, abstractions for machine learning projects and some of the unspoken challenges she's seen in the ML development process. Also prediction markets and Harry Potter.  Watch the video: https://www.youtube.com/watch?v=uQrR0KPq3RQ  Join our Discord community: https://discord.gg/tEYvqxwhah  Relevant Links:   Noa's talks:  🎥 The Quick & Dirty AI Startup: https://www.youtube.com/watch?v=HZ_LRxP3ep0 🎥 Choosing the Right Machine Learning Abstraction for your Business Needs – https://www.youtube.com/watch?v=C0gN47H91HM  ➡️ Noa Weiss on LinkedIn – https://www.linkedin.com/in/noa-weiss/ ➡️ Noa Weiss on Twitter – https://twitter.com/NWeiss   🌐 Noa's website: https://www.weissnoa.com  Recommendation Links:  - Unsong – https://unsongbook.com/ - Harry Potter & The Methods of Rationality – https://www.hpmor.com/  🌐 Check Out Our Website! https://dagshub.com    Social Links:   ➡️ LinkedIn: https://www.linkedin.com/company/dagshub   ➡️ Twitter: https://twitter.com/TheRealDAGsHub   ➡️ Dean Pleban: https://twitter.com/DeanPlbn

In this episode, I speak with Assaf Pinhasi, ML engineering and MLOps consultant extraordinaire! Assaf was the VP R&D at Zebra Medical Vision, and built the PayPal Risk organization's Big Data Platform. We dive into building ML infrastructure from scratch 10 years ago vs. today, best practices involved in building teams to support machine learning models in production, and the future of generative models.   Watch the video: https://youtu.be/tSbuDA5tMxQ Join our Discord community: https://discord.gg/tEYvqxwhah ➡️ Assaf Pinhasi on LinkedIn – https://www.linkedin.com/in/assafpinhasi/  🌐 Check Out Our Website! https://dagshub.com    Social Links:   ➡️ LinkedIn: https://www.linkedin.com/company/dagshub   ➡️ Twitter: https://twitter.com/TheRealDAGsHub   ➡️ Dean Pleban: https://twitter.com/DeanPlbn