Abstract: 

ML Governance is often discussed either in abstract terms without practical details. This talk will focus on the day-to-day realities of ML Governance. How much documentation is appropriate? Should you have manual sign-offs? If so, when and who should perform them? Most importantly, what is the point of all this governance and how much is too much?

We'll see that the core of ML governance is ensuring the right kind of risk trade-off decisions are owned explicitly by the most appropriate roles. Many ML governance failures arise from overlooking risks and failing to take risk mitigation steps. We'll walk through a template process that can be used to manage risk and reinforce best practice.

From this talk you will learn:

- What ML Governance is meant to achieve
- How to get started with a template process
- The role of documentation (and especially Google Model Cards)
- Which roles have what responsibilities
- The relevance of a governance board

Bio: 

Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.