MLOps empowers data scientists and app developers to bring ML models to production. This session presents how to use MLOps in Azure to track, version, audit, certify, and re-use every asset in your ML lifecycle and streamline the use of each resource. With practical examples of asset management and orchestration services for your ML model training and deployment workflows, you will learn about the Azure DevOps Machine Learning extension, best practices for data scientists to work in topic branches off master, when code is pushed to a Git repo, how to trigger a CI (continuous integration) pipeline, and how to provision ML workspaces, compute targets, datastores as infrastructure-as-code.
Stefano Tempesta is a technologist working at the crossroads of Web2 and Web3, to make the Internet a more accessible, meaningful, and inclusive space. Stefano is an ambassador of the use of AI and blockchain technology for good purposes. A former advisor to the Department of Industry and Science, Australia, on the National Blockchain Roadmap, he is co-founder of Aetlas, a decentralized climate action and sustainability network with a mission to source Verified Carbon Units for liquidity and carbon asset monetization.
Stefano is a lecturer at RMIT University on courses about AI and blockchain technology, and he has co-authored the book “Blockchain Applied”.