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Machine Learning Ops

MLOps constitute best practices for developing, deploying, and monitoring high precision Machine Learning models.

Machine learning operations (MLOps) is the umbrella term for best practices surrounding machine learning. The guiding principles integral to MLOps include things such as:

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  • ML projects should be collaborative
  • They should be reproducible
  • They should be tested and monitored
  • They should be continuous, so retraining a model is as effortless as possible

MLOps follow a similar process as DevOps-- that is to say, it drives a seamless integration between your development lifecycle and the overall operations process in handling Big Data. The end goal is to derive insights using machine learning techniques that you can trust and deploy more efficiently.

Benefits of MLOps:

  • Ability to reproduce workflows and ML Models
  • Enhanced efficiency in the management of machine learning lifecycle
  • More effective resource management
  • Easy deployment of ML models yielding high precision insights