Data Team Success
Claim This Podcastby Ross WEBB
Podcast Overview
<p>Welcome to ML Team Success — the show for ML engineers, data scientists, and MLOps practitioners who want to actually ship AI that works in production.</p> <p>I'm Ross Webb. I've led data product teams and ML engineering teams at places like Amazon and Just Eat, building platforms used by thousands of professionals. I've seen what works, what breaks, and why 90% of ML projects never make it to production.</p> <p>Each episode: real conversations with practitioners who are solving the hard problems — MLOps, model deployment, inference at scale, data pipelines, and the shift to AI engineering and agentic systems.</p> <p>No theory for theory's sake. No hype. Just the stuff that matters when you're trying to get models into production and keep them there.</p> <p>Subscribe and join a growing community of ML practitioners who build things that actually work.</p>
Language
🇺🇲
Publishing Since
7/22/2023
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Recent Episodes
March 24, 2026
The Complete MLOps Lifecycle: From Data to Deployment | Phillip Mortimer
<p>90% of ML projects never make it to production. That's not a talent problem — it's an MLOps problem.</p> <p>Phillip Mortimer is a computer scientist, ex-Chief Scientist and CTO at a London fintech, and one of the only people teaching MLOps at university level (Dauphine University, Paris — 5 years running).</p> <p>In this episode, Phillip walks through the complete MLOps lifecycle:</p> <p>• Data preparation — why EDA is the most forgotten step, and why data pipelines still matter in the LLM era</p> <p>• Model building — Karpathy's 5-stage training cookbook: become one with your data → fit a baseline → overfit → regularise → squeeze out the juice</p> <p>• Experiment tracking — MLflow, Weights & Biases, model registries, and model cards</p> <p>• Deployment — real-time vs batch, Docker containers, inference optimisation with ONNX, vLLM, and TensorRT</p> <p>• Monitoring — data drift, feedback loops, and keeping models relevant</p> <p>• The future — why MLOps is shifting to AI engineering, and why agentic AI is the real breakthrough</p> <p>Key stat: 90% of ML infrastructure cost is inference, not training. If you're not optimising your serving layer, you're burning money every day.</p>
March 15, 2026
From Jupyter Notebook to Production: A Data Scientist's MLOps Journey | Anastasiia Kulakova
<p>Your model only matters if it connects to the business. But when you're a data scientist learning MLOps on the fly, experimenting on live infrastructure is terrifying.</p> <p>Anastasiia Kulakova is an Amsterdam-based data scientist at JetLakes, a fast-growing energy and mobility startup. She shares her candid journey from Jupyter Notebook to production-ready ML.</p> <p>In this episode:</p> <p>• Why "we have the data" from stakeholders rarely means what you think</p> <p>• How to build your own MLOps learning sandbox without breaking production (GitHub Actions, Heroku, DigitalOcean)</p> <p>• The reality of being a data generalist at a startup — wearing every hat from model training to Scrum Master</p> <p>• How JetLakes uses predictive algorithms to balance the energy grid through EV charging optimisation</p> <p>• Virtual power plants: turning parked electric vehicles into grid-scale flexibility</p> <p>• Why the precision economy is coming to energy — and what that means for data teams</p>
November 27, 2024
Data Leaders Conduct Success Like Orchestra Conductors - Here's How
<p>Just like a masterful orchestra needs a skilled conductor, successful data teams require leaders who can harmonize diverse talents into powerful performance. In this episode, top data leaders share their strategies for orchestrating success in modern data organizations.</p> <p>0:00 - Introduction: The Orchestra of Data Leadership</p> <p>1:27 - Meet Our Conductor-Level Leaders</p> <p>2:13 - Bridging Academia & Industry with Ali Saad</p> <p>4:33 - Practical Skill Development with Anastasia Kulakova</p> <p>8:19 - Leadership Journey Insights with Julie Montel</p> <p>11:15 - Data Storytelling Mastery with Promit Ray</p> <p>13:49 - Key Takeaways & Action Steps</p> <p><br /></p> <p>You'll discover:</p> <ul><li>How to bridge the gap between academic knowledge and real-world application</li><li>Practical strategies for gaining hands-on MLOps experience</li><li>Multiple paths to data leadership positions</li><li>Essential data storytelling techniques for stakeholder communication</li></ul> <p>Featured Data Leaders:</p> <p>Ali Saad - Data Leader</p> <p>Anastasia Kulakova - MLOps Expert</p> <p>Julie Montel - Data Team Leader</p> <p>Promit Ray - Data Science Leader</p> <p>Ready to conduct your own data success story? Join our Data Chiefs community where we provide structured support, peer learning, and proven frameworks to help you advance your data leadership career.</p> <p>Visit www.data-chiefs.com to start your journey.</p> <p>#DataLeadership #DataScience #DataStrategy #CareerGrowth #MLOps</p>
28 total episodes available
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Frequently asked questions
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- What is Data Team Success?
<p>Welcome to ML Team Success — the show for ML engineers, data scientists, and MLOps practitioners who want to actually ship AI that works in production.</p> <p>I'm Ross Webb. I've led data product teams and ML engineering teams at places like Amazon and Just Eat, building platforms used by thousands of professionals. I've seen what works, what breaks, and why 90% of ML projects never make it to production.</p> <p>Each episode: real conversations with practitioners who are solving the hard problems — MLOps, model deployment, inference at scale, data pipelines, and the shift to AI engineering and agentic systems.</p> <p>No theory for theory's sake. No hype. Just the stuff that matters when you're trying to get models into production and keep them there.</p> <p>Subscribe and join a growing community of ML practitioners who build things that actually work.</p> - How often does this podcast release new episodes?
This podcast updates weekly.
- Where can I listen to this podcast?
This podcast is available on 9 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
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