The Complete MLOps Lifecycle: From Data to Deployment | Phillip Mortimer
- S6E28
- 39:32
- March 24th 2026
90% of ML projects never make it to production. That's not a talent problem — it's an MLOps problem.
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).
In this episode, Phillip walks through the complete MLOps lifecycle:
• Data preparation — why EDA is the most forgotten step, and why data pipelines still matter in the LLM era
• Model building — Karpathy's 5-stage training cookbook: become one with your data → fit a baseline → overfit → regularise → squeeze out the juice
• Experiment tracking — MLflow, Weights & Biases, model registries, and model cards
• Deployment — real-time vs batch, Docker containers, inference optimisation with ONNX, vLLM, and TensorRT
• Monitoring — data drift, feedback loops, and keeping models relevant
• The future — why MLOps is shifting to AI engineering, and why agentic AI is the real breakthrough
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.
MLTeam Success
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.
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.
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.
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.
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