Best Practices: Machine Learning Observability - Ankit Rathi, Yatin Bhatia | PyData Global 2021

Best Practices in Machine Learning Observability Speakers: Ankit Rathi, Yatin Bhatia Summary As more and more organizations are turning to machine learning (ML) to optimize their businesses, they soon realize that building ML proof of concepts in the lab is very different from making models that work in production. Things keep changing in production, impacting model perfomance. Lets explore ways to keep ML models effective in production using ML observability and its best practices. Ankit Rathi’s Bio Ankit Rathi is a data science architect, published author & well-known speaker. His interest lies primarily in building end-to-end data science applications/products following best practices of data engineering and architecture. Ankit’s work at SITA aero has revolved around building FlightPredictor product & strengthening the CoE capability. Earlier as a Principal Consultant at Genpact HCM, Ankit architected and deployed machine learning pipelines for various clients acro