MLflow: Platform for Complete Machine Learning Lifecycle by Quentin Ambard

Machine learning development brings many new complexities beyond the traditional software development lifecycle. ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to ensure reproducibility, especially when it comes to deploy these models in production! Join the session to discover how the MLflow open source platform can help you to: Keep track of experiments runs and results across frameworks Register projects and quickly reproduce your runs Quickly productionize models using Docker containers, Azure ML, or Amazon SageMaker I have been working in several french start-up (commercial prospecting, predictive sales, supplier management). I currently work for Databricks as Solutions Architect, driving customer to succes in their big data and data science project.