07. Model Deployment and Monitoring: Putting ML into Production

PRTDM2-785 AI Trading C2 L1 Vid9 Model Deployment And Monitoring

Deploying Machine Learning Models to Production

After building a high-performing ML model, the next essential step is production deployment:

  • Purpose of Production: Make the model's predictions accessible to users by integrating it into practical applications, like product recommendations on websites.

  • Model Deployment Scenarios:

    • Deploying in large enterprise environments demands robust infrastructure with rapid prediction capabilities.
    • For personal use, ensure models are correctly stored to prevent outdated predictions.

  • Local Deployment:

    • For small-scale or personal projects, models can run from a local machine.

  • Steps to Professionalize Local Models:

    • Create an API to make your model easy to call.
    • Organize code into separate scripts or files for each step of the pipeline.

  • Benefits of API: By wrapping the model's prediction code into a Python script and function, the deployment becomes clean and straightforward, enriching usability and monitoring.

Understanding these aspects is key in ensuring a model's effectiveness beyond development.

After successfully building and tuning your high-performance model, what key steps should you consider when putting your model into production?
SOLUTION:
  • Use version control to keep track of different iterations of your model.
  • Create an API to allow other applications to request predictions from the model.
  • Document your model's features and the rationale behind your design choices.

What is one of the most impactful steps you can take to professionalize and productionalize your models?
SOLUTION: Building an API that allows users to access the model's predictions.

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