MLFlow: Introduction to Implementation
Deploying ML models in production is a complicated process, and experts estimate that as many as 90 percent of ML models never make it into production. Are you also in the same boat? Are you having difficulty transforming the Jupyter notebook's code to Production code?
Not able to manage the end-to-end machine learning lifecycle?
And many more production issues related to Machine Learning?
If your answer is "Yes" to the above questions, join our upcoming hands-on workshop on "MLFlow," an open-source platform for managing the end-to-end machine learning lifecycle.
What will we learn?
MLflow Tracking: Record and query experiments: code, data, config, and results
MLflow Projects: Package data science code in a format to reproduce runs on any platform
MLflow Models: Deploy machine learning models in diverse serving environments
About the Speaker
Ladle is a Full-Stack Data Scientist and a Senior Manager at Genpact. He is also co-founder of Unlearning Relearning community, through which he is educating hundreds of rural and small-town students and spreading awareness about Climate Change. Currently, he is the site lead of the Hyderabad Genpact Data Science & Insights team, leading a team of Data Scientists and Data Engineers. He is a passionate educator and speaker, having delivered keynote sessions at many meetups and forums in Hyderabad, India.