The optimal strategy for ensuring high availability of the deployed machine learning model in a Databricks environment using MLflow is to implement load balancing for the deployed model‘s REST API. This approach offers several benefits:

• Scalability: Load balancing distributes incoming requests across multiple worker nodes, preventing any single node from becoming a bottleneck and ensuring the model remains responsive under heavy traffic.
• Fault tolerance: In the event of a node failure, the load balancer can redirect requests to other healthy nodes, thereby minimizing downtime and maintaining service availability.
• Flexibility: Load balancing can be seamlessly integrated with Databricks MLflow Model Serving, providing a managed solution for scaling and high availability.

Other options considered:

• Scheduling restarts: This can lead to service interruptions and does not address runtime issues.
• Separate workspace: While it isolates production from development, it does not directly tackle load and fault tolerance needs.
• Increasing cluster size: While it can improve capacity, it does not offer fault tolerance or address single-node bottlenecks.

Implementing load balancing is the most efficient and reliable method to ensure high availability, as it scales seamlessly, handles failures gracefully, and leverages Databricks capabilities within a single workspace. Additional strategies like autoscaling and proactive monitoring can further enhance system stability.