Machine Learning Lifecycle, Continuous Evaluation & DevOps - Scaling your Machine Learning Efforts
November 19, 2019
Everyone hears about machine learning (ML) & artificial intelligence (AI) while you are building the models. You spend weeks/months working on something, prototyping and when things “are done”, it needs to be deployed in production ASAP – and that is just the tip of the iceberg. We use ML models when we need to find patterns without explicitly programming machines to do so. Data scientists usually do not have a software engineering background, testing ML is tricky and all the other problems related to ML in production, ML components can drink from the same source of the devops movement. Do we need to talk about CI/CD for ML? Yes, please, but we need to talk also about Continuous Evaluation! How can we test and debug ML? Create a safe environment for data scientists is important, but why exactly? How can we package, deploy and serve ML models? By the end of this talk, you will understand more about ML lifecycles, the AI hype and feel more comfortable to answer these questions and help your organization move faster. Thiago also promises a ML testing and building demo… may the demo gods help us!