Adopting an MLOps mindset

Developing Machine Learning Models for Production

Sinan Ozdemir

Data Scientist, Entrepreneur, and Author

MLOps

  • The process of automating and streamlining the ML workflow from experimentation to production
  • Ensures that ML experiments are properly tested and ready to be deployed and scaled

mlops

Developing Machine Learning Models for Production

ML experiments

ML experiments involves testing models and determine which one is best

  • MLOps includes model experimentation
  • Evaluating models on different datasets
  • Careful model selection essential
  • Selection process may be time-consuming
  • Ensures project success
Developing Machine Learning Models for Production

From experiments to production

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What Makes an ML Experiment Ready for Production?

  • Tested and validated using appropriate metrics
  • Proper documentation
  • Performing monitoring is in place
  • Production environment is secure and scalable
Developing Machine Learning Models for Production

Why most ML experiments fail

There are many reasons why this might happen:

  • Lack of clear goals and objectives
  • Poor data quality
  • Complex model architectures
  • Insufficient training data
  • Overfitting or underfitting
Developing Machine Learning Models for Production

Technical debt

Code written in a hurry without proper testing or validation or missing/incomplete/out-of-date documenation

  • Costly errors or bugs can occur if not addressed early on.
  • Avoid technical debt by writing proper code and documentation from the start.

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Developing Machine Learning Models for Production

Let's practice!

Developing Machine Learning Models for Production

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