I Made a Model, Now What?

Last October, I had the pleasure of giving a talk at PyData Atlanta - a fantastic meetup that I highly recommend for anyone in the Atlanta area. While I’d given lightning talks before, this was my first longer format presentation, and the feedback was positive.

Key Themes

The presentation focused on three critical aspects of model deployment that every data scientist should consider:

  1. Getting models into production successfully
  2. Ensuring models continue to work in production
  3. Making model degradation observable and manageable

Understanding Your Organization

Success in model deployment often comes down to understanding your organization’s structure and processes. There’s inevitably a handoff between:

  • The data scientist who creates the model
  • The operations/engineering team that manages it in production

Understanding what “production” means in your context, how to make the deployment process smooth, and how to ensure proper monitoring are crucial responsibilities of the data scientist.

Practical Solutions

The Pipeline Approach

One successful strategy I’ve employed is packaging as much of the data processing as possible into a scikit-learn pipeline object. This approach offers several benefits:

  • Creates a pickle-able artifact
  • Can be easily passed between teams
  • Owned by data science
  • Provides clear input/output specifications
  • Gives ops/engineering a well-defined object to work with

The Observability Challenge

While treating the model as a black box can be operationally useful, it creates an observability challenge:

  • How do ops teams know when something is wrong?
  • What metrics should be tracked?
  • How can data scientists stay engaged post-deployment?

Best Practices

To address these challenges:

  1. Implement robust logging of model behavior and metadata
  2. Create dashboards for monitoring model performance
  3. Establish clear communication channels between teams
  4. Define specific criteria for model health
  5. Set up automated alerts for potential issues

The Path Forward

The key to successful model deployment isn’t just about the technical implementation - it’s about creating a sustainable system where:

  • Data scientists remain engaged after deployment
  • Operations teams understand what to monitor
  • Both groups have the tools they need to succeed
  • Clear ownership and responsibilities are established

Remember: throwing models over the wall and hoping for the best isn’t a strategy. Success requires ongoing collaboration, monitoring, and maintenance.