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.

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.

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.

In this episode:

• Why "we have the data" from stakeholders rarely means what you think

• How to build your own MLOps learning sandbox without breaking production (GitHub Actions, Heroku, DigitalOcean)

• The reality of being a data generalist at a startup — wearing every hat from model training to Scrum Master

• How JetLakes uses predictive algorithms to balance the energy grid through EV charging optimisation

• Virtual power plants: turning parked electric vehicles into grid-scale flexibility

• Why the precision economy is coming to energy — and what that means for data teams

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.

0:00 - Introduction: The Orchestra of Data Leadership

1:27 - Meet Our Conductor-Level Leaders

2:13 - Bridging Academia & Industry with Ali Saad

4:33 - Practical Skill Development with Anastasia Kulakova

8:19 - Leadership Journey Insights with Julie Montel

11:15 - Data Storytelling Mastery with Promit Ray

13:49 - Key Takeaways & Action Steps

You'll discover:

• How to bridge the gap between academic knowledge and real-world application
• Practical strategies for gaining hands-on MLOps experience
• Multiple paths to data leadership positions
• Essential data storytelling techniques for stakeholder communication

Featured Data Leaders:

Ali Saad - Data Leader

Anastasia Kulakova - MLOps Expert

Julie Montel - Data Team Leader

Promit Ray - Data Science Leader

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.

Visit www.data-chiefs.com to start your journey.

#DataLeadership #DataScience #DataStrategy #CareerGrowth #MLOps