Implement an early end-to-end integration to iterate faster by using small and valuable increments. Create an entire deployment process, continuous monitoring, and management strategy.

Agenda:

• Evaluate the best approach to train and serve models to production – batch / real-time.
• Adopt ML System Architecture and Writing Production-Ready Codes.
• Model Deployment, Monitoring, Maintenance Measures – Health Checks, Data Drifts.
• MLOps for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.
• Implement feedback loops to improve the model’s performance.