A Framework for a Successful Continuous Training Strategy in MLOps

Discover a comprehensive framework for implementing a successful continuous training strategy in machine learning production environments. Explore the challenges of maintaining model accuracy and reliability in dynamic data environments where concept drift is common. Learn from Or Itzary, Chief Architect at Superwise, as he shares insights gained from 8 years of experience as both a data scientist and ML engineer. Examine different approaches and methodologies for continuous retraining, weighing their pros, cons, and associated costs. Gain valuable production-driven insights to optimize your retraining processes and avoid common pitfalls such as manual overtraining or defaulting to unnecessary retraining. Equip yourself with the knowledge to develop a more efficient and effective continuous training strategy for your ML models in production.

A Framework for a Successful Continuous Training Strategy