10/22/20 #2 Matei Zaharia - Machine Learning at Industrial Scale: Lessons from the MLflow Project

Matei Zaharia - Machine Learning at Industrial Scale: Lessons from the MLflow Project

Although enterprise adoption of machine learning is still early on, many enterprises in all industries already have hundreds of internal ML applications. ML powers business processes with an impact of hundreds of millions of dollars in industrial IoT, finance, healthcare and retail. Building and operating these applications reliably requires infrastructure that is different from traditional software development, which has led to significant investment in the construction of “ML platforms” specifically designed to run ML applications. In this talk, I’ll discuss some of the common challenges in productionizing ML applications based on experience building MLflow, an open source ML platform started at Databricks. MLflow is now the most widely used open source project in this area, with over 2 million downloads a month and integrations with dozens of other products. I’ll also highlight some interesting problems users face that are not covered deeply in current ML systems research, such as the need for “hands-free” ML that can train thousands of independent models without direct tuning from the ML developer for regulatory reasons, and the impact of privacy and interpretability regulations on ML. All my examples will be based on experience at large Databricks / MLflow customers.