deployment

Machine learning model deployments don’t always go according to plan. When a newly deployed model starts producing unexpected results, degrades in performance, or causes system instability, having robust ML model rollback strategies becomes critical for maintaining business continuity and user trust. The complexity of modern ML systems means that rollback procedures require careful planning, automated … Read more

When machine learning models fail in production, the ability to quickly and effectively roll back to a previous stable version can mean the difference between minor service disruption and catastrophic business impact. Rolling back failed machine learning model deployments is a critical skill that every ML operations team must master, yet it presents unique challenges … Read more