In this episode, I chatted with Uri Goren, founder and CEO of Argmax, about Machine Learning and the future of digital advertising in a world moving away from cookies due to privacy laws like GDPR and CCPA. We chat about challenges in maintaining personalized ads while respecting user privacy, and new methods like probabilistic models and contextual features to cover some of the gap left by removing cookies. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00...

In this episode, I speak with Han-Chung Lee, a machine learning engineer with a lot of interesting takes on ML and AI. We dive into the buzz around natural language processing and the big waves in generative AI. They chat about how newcomers are racing through NLP’s history, mixing old school and new tech, and the shift towards smarter databases. Han-Chung breaks it down with his straightforward takes, making complex AI trends feel like coffee chat topics. It’s a perfect listen for anyone...

In this episode, I had the pleasure of speaking with Mila Orlovsky, a pioneer in medical AI. We delve into practical applications, overcoming data challenges, and the intricacies of developing AI tools that meet regulatory standards. Mila discusses her experiences with predictive analytics in patient care, offering tips on navigating the complexities of AI implementation in medical environments. This episode is packed with actionable advice and forward-thinking strategies, making it essential...

In this episode, I had the pleasure of speaking with Jason Liu, an applied AI consultant and the creator of Instructor – an open-source tool for extracting structured data from LLM outputs. We chat about LLM applications, their challenges, and how to overcome them. We also dive into Instructor, making LLMs interact with existing systems and a bunch of other cool things. Join our Discord community: https://discord.gg/tEYvqxwhah ➡️ Jason Liu on Twitter – https://twitter.com/jxnlco 🤖...

In this live episode, I'm speaking with Jinen Setpal, ML Engineer at DagsHub about actually building, deploying, and monitoring large language model applications. We discuss DPT, a chatbot project that is live in production on the DagsHub Discord server and helps answer support questions and the process and challenges involved in building it. We dive into evaluation methods, ways to reduce hallucinations and much more. We also answer the audience's great questions.

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/

In this episode, I had the pleasure of speaking with Yuval Gabay, MLOps Engineer at WSC Sports. Yuval builds better infrastructure and automation for developing, training, and deploying machine learning models at scale, with a focus on video data. We talk about MLOps methodologies, standardizing deployment in the organization, and closing the loop back from production into training. Watch the video: https://youtu.be/3m__nRuifsQ Join our Discord community: https://discord.gg/tEYvqxwhah...

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...

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:...

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. Sherya 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:...

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...

In this episode, I speak with David Marx, Distinguished Engineer at Stability AI. This talk dives into how David got into machine learning, open-source software, and Stability AI.   We discuss following your curiosity, and what it takes to deploy a model like Stable Diffusion to production.   Watch the video: https://youtu.be/49dsoDK1KCA Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:   🛌 Big Sleep by Ryan Murdock –...

In this episode, I speak with Logan Kilpatrick, Julia Language Developer Community Advocate. We talk about machine learning at NASA and how he discovered Julia as a student, the age-old Julia vs. Python debate, and how to get into a new scientific and technical field.  It was absolutely awesome! Check it out.   Watch the video: https://www.youtube.com/watch?v=3kgRN8hJIro Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:   ➡️ Logan Kilpatrick on LinkedIn –...

In this episode, I speak with Guy Smoilovsky, my friend, Co-Founder, and the CTO of DagsHub. We talk about quantum computing and AGI, concrete approaches for automating ML deployment, and how DagsHub came to be.   Watch the video: https://www.youtube.com/watch?v=67dByhXPT5g Join our Discord community: https://discord.gg/tEYvqxwhah  Relevant Links:   ➡️ Guy Smoilovsky on LinkedIn – https://www.linkedin.com/in/guy-smoilovsky/ ➡️ Guy Smoilovsky on Twitter –...

In this episode, I speak with Dean Langsam, Data Scientist at SentinelOne and one of the organizers of PyData in Israel. We chat about imposter syndrome, the best field in machine learning, why XGBoost is the best model, and the fact that most organizations have too much data.  It was fascinating for me, so I hope you enjoy it too.   🎬 Watch the video: https://www.youtube.com/watch?v=Akz_PpDdLlQ Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:  ⭐️ Join PyData...

In this episode, I had the pleasure of speaking with Jacopo Tagliabue, Director of AI at Coveo. We talk about Reasonable Scale MLOps, how to approach building your ML platform, and how quickly you might hit the limits of model deployment (hint: it's pretty surprising) Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links: ➡️ Jacopo on LinkedIn – https://www.linkedin.com/in/jacopotagliabue/ ➡️ Jacopo on Twitter – https://twitter.com/jacopotagliabue Recommendation...

In this episode, I had the pleasure of speaking with Goku Mohandas, founder of Made With ML. Goku has an incredible amount of experience building and teaching the community about machine learning and MLOps systems. We dive system thinking and solving for ML workflow, his journey in the machine learning world, and how he chooses what to learn next. We discuss the most common mistakes he's seen in productionizing ML models and why building models no one will use is not necessarily bad....

In this episode, I had the pleasure of speaking with Kyle Gallatin, a Machine Learning Software Engineer at Etsy. We talk about how he built the machine learning platform at Etsy, experimentation in production (yes, you heard right), and how to optimize model performance at very large scales.  It was awesome, and I'm sure many of you can learn a ton from this one!   Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:   ➡️ Kyle on LinkedIn –...

In this episode, I'm speaking with Charlene Chambliss, Software Engineer at Aquarium. Charlene has vast experience getting NLP models to production. We dive into the intricacies of these models and how they differ from other ML subfields, the challenges in productionizing them, and how to get excited about data quality issues.   Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:  ➡️Charlene on LinkedIn – https://www.linkedin.com/in/charlenechambliss/ ➡️Charlene on...

In this episode, I'm speaking with the one and only, Yannic Kilcher! We talk about sunglasses 😎, the value and methodologies behind taking complex machine learning research, and making the idea accessible and digestible. We also discuss reproducibility in machine learning and the moving between research and entrepreneurship.   If you haven't seen his videos you should definitely check them out on his YouTube channel (https://www.youtube.com/c/YannicKilcher).   Join our Discord community:...

In this episode, we dive into the challenging but very important topic of getting data scientists to write better code. How to approach complex machine learning projects and break them down, and why growing unicorns 🦄 is better than hunting them. Check out this is an awesome conversation with Laszlo Sragner, Founder at 🔥 Hypergolic.   Join our Discord community: https://discord.gg/tEYvqxwhah ---  Timestamps:  00:00 Podcast intro  01:00 Guest introduction  02:34 Why is writing better...

In this episode, I'm speaking with Lee Harper, Principal Data Scientist at Catapult Systems. Lee holds a Ph.D. in Physical and Theoretical Chemistry. Lee is a teacher-turned-data scientist. We cover the various entry paths into the world of data science, the value of background diversity, security in ML production, and even AI fairness.   Join our Discord community: https://discord.gg/tEYvqxwhah  ---  Timestamps:  00:00 Podcast intro  01:00 Guest introduction  01:39 How did you get...

In this episode, I'm speaking with Roey Mechrez from BeyondMinds. Roey holds a Ph.D. in Electrical Engineering, with vast experience in computer vision and deep learning research. We discuss the challenges of gluing together infrastructure solutions for an end-to-end ML platform, as well as generating monitoring insights for non-technical stakeholders and combating catastrophic forgetting. Join our Discord community: https://discord.gg/tEYvqxwhah ---  Timestamps:  00:00 Podcast intro ...