MLOps is essentially the application of DevOps principles to machine learning.

It provides a seamless flow throughout the machine learning lifecycle, infusing tried-and-true strategies from software engineering.

The goal is not just to construct machine learning models but to ensure they are deployed, overseen, and maintained efficiently when in use.

Key Components:

Continuous Integration (CI): Automated checks for ML code and workflows.

Continuous Delivery (CD): Streamlining the rollout of models.

Model Versioning: Maintaining a record of various model and data iterations.

Model Monitoring: Real-time tracking of model efficiency and status.

Model Retraining: Regularly updating models with fresh data to keep them current.

Scalability & Serving: Ensuring models can accommodate and respond to actual user demand.

Collaboration: Tools and platforms to foster teamwork and consistency in ML processes.

Through MLOps, the deployment of dependable and superior ML models to production is accelerated and ensured.