With the race to adopt artificial intelligence (AI) into a business, some organizations are under the misconception that simply deploying an AI model is the finish line. In reality, operationalizing an AI model through machine learning operations (MLOps) is a continuous process. The following 4 elements are key components of operationalizing your model after deployment, ideally through an AI development platform.

Monitoring: Effective monitoring helps make sure that an AI model continues to perform as expected in changing circumstances. It is crucial to identify and adapt to any deviations in data or performance early to maintain reliability and trust.

Maintenance: Implementing proactive maintenance on AI models is a core part of operationalizing them. Maintenance involves updating models, fixing bugs, and adapting to changing environments.

Model Retaining: Retraining is a process of refitting the model to proactively work to increase accuracy.

Governance: Governance is the process of establishing ethical guidelines and security practices, and adhering to regulatory requirements. Governance is not optional.

Check out this Checklist to learn more