About this webinar
Governance for machine learning is essential and must form the backbone of MLOps systems in order to satisfy financial, legal and ethical obligations. However, getting to this point is a key challenge for FS organisations. From a business stakeholder’s perspective, governance is likely to slow down model production and cost the business money. And from a data scientist’s perspective, governance is a lot of bureaucracy that negatively impacts their productivity.
Implementing governance of not just technology but also people and processes enables ML deployment to scale across an organisation and generate true value. In this session, FSI Lead Richard Jarvis will dive into the challenges he has seen from our customers and how to overcome them. There are a number of key tools and techniques that have proven to be successful in companies we have worked with, that help in reducing internal governance timelines by up to six months. Ed Shee, Head of Developer Relations, will join Richard and share what he has learnt from working closely with the wider developer community. They’ll also discuss the research from Seldon’s Engineering Director into the world of MLSecOps.
We’ll also look to the future and explore what financial services will face in terms of regulation, both what will need to be monitored and reported as well as how those needs can be met. The ML project of the future needs to have complete trust and transparency from internal and external stakeholders to succeed.
What you'll learn
- What is the state of ML adoption and capabilities in FSI?
- What regulations are incoming that the industry needs to prepare for?
- What tools will become essential to mitigate governance risk?
- How do you create agile scale in legacy environments?
- What does this mean for your organisation’s future?