What are some effective strategies for deploying machine learning models?

What are some effective strategies for deploying machine learning models?

There are several strategies for deploying ML models, including batch processing, real-time inference, and A/B testing.

Can you explain the difference between batch processing and real-time inference?

Sure! Batch processing involves running predictions on a large dataset at once, while real-time inference provides predictions instantly as data comes in.

What are the challenges associated with real-time inference?

Challenges include latency, scalability, and ensuring the model can handle varying input data in real-time.

How can I monitor the performance of a deployed model?

You can monitor performance using metrics like accuracy, latency, and user feedback, along with tools for logging and visualization.