Businesses are investing heavily in machine learningMachine Learning and artificial intelligenceArtificial Intelligence (AI). They are increasingly looking for machine learning operations (MLOps) to help them scale their internal data science practice across multiple groups and business lines. Organizations can use MLOpsMLOps to automate and standardize processes across teh ML lifecycle. And yet, far too many organizations do not know how to define MLOpsMLOps. They don’t know how to implement it. They are left with disjointed model research and production flows that deliver sub-par results. This chapter introduces MLOpsMLOps, a holistic approach to scaling the production of models across modern enterprises. We will cover underlying technologies and guiding principles found in MLOpsMLOps.

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Machine Learning Operations

  • Pramod Gupta,
  • Naresh Kumar Sehgal,
  • John M. Acken

摘要

Businesses are investing heavily in machine learningMachine Learning and artificial intelligenceArtificial Intelligence (AI). They are increasingly looking for machine learning operations (MLOps) to help them scale their internal data science practice across multiple groups and business lines. Organizations can use MLOpsMLOps to automate and standardize processes across teh ML lifecycle. And yet, far too many organizations do not know how to define MLOpsMLOps. They don’t know how to implement it. They are left with disjointed model research and production flows that deliver sub-par results. This chapter introduces MLOpsMLOps, a holistic approach to scaling the production of models across modern enterprises. We will cover underlying technologies and guiding principles found in MLOpsMLOps.