Webinar: AutoML and MLOps for Scalable Machine Learning Implementations

Recent advances in machine learning have enabled the large-scale automation of a broad spectrum of activities in the machine learning processes. MLOps methodologies help reduce the cycle time for the deployment of ML models in production while ensuring quality and reliability by incorporating best practices from software engineering.

Join us to know some of the key advances in the ways we develop and manage the machine learning life cycle and learn about -

1. MLOps principles
2. How to manage the machine learning model lifecycle in production systems
3. Applications of AutoML across the various stages of ML Lifecycle
4. Why drift detection is necessary for production of ML models