Browse Lessons

ML tooling in large companies

MLOps Problems in different size companies

Friction Between Data Scientists and Software Engineers

Provenance and Reproducibility in Machine Learning; what is it and why you need it?

How Phil Winder got into Data Science and Software Engineering

MLOps and Monitoring

Bare necessities for getting an ML model into production

Hierarchy of MLOps Needs

Building an MLOps Team? Key ideas to keep in mind

Automatically Retrain Machine Learning Models? Are best practices worth it?

Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3

What should we be optimizing for?

The problem with too many smart ML engineers

Explainability, Black boxes and EU white paper on reproducibility

Life purpose and too many spreadsheets

Common sense AI/ML governance with Charles Radclyffe

Automation, UBI, and taxes with Charles Radclyffe

What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2

MLOps lifecycle description

MLOps Manifesto with Luke Marsden from Dotscience

Remote Collaboration as a Data Scientist

Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1