ML Infrastructure Part 2:
Model Deployment

Machine learning model deployment introduces new challenges to the traditional software development life cycle, and current DevOps workflows may not be prepared to meet those challenges. 

This whitepaper will discuss:

  • Features needed for a successful ML deployment platform
  • Pitfalls of traditional software development in ML deployment 
  • Next steps for working toward ML sophistication 

Machine Learning Model Deployment

Building on part 1 of our ML Infrastructure whitepaper series, part 2 explores the misconception that relying on traditional methods of software development will work for the machine learning life cycle.

The paper outlines best practices for ensuring efforts to deploy models are successful, including these factors:

  • Repo Integration
  • System Dependencies
  • API & GUI Deployment
  • Versioning

Ensure your ML program does not stall at deployment. 

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