ML Ops and the promise of Machine Learning at Scale

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Enterprise interest in artificial intelligence, fueled by machine learning, continues to expand. In its most recent survey on AI Adoption in the Enterprise, O’Reilly found that 85% of organizations are at least exploring the use of artificial intelligence. By James Kulich.

The bad news: Too many artificial intelligence projects fail. Currently, an estimated 78%-87% of artificial intelligence projects never make it into production.

Machine learning models are dynamic. The initial model development stage is often quite experimental, involving many iterations of candidate models. Doing this effectively at scale requires good version control. Data used to develop models must be validated and appropriately split into training and testing sets. Models must be validated, both in terms of their technical performance and in terms of their effectiveness in addressing the needs for which they were created.

The article also deals with:

  • In the beginning there was DevOps
  • Taking the next step to ML Ops
  • ML Ops and value creation

Most recently, ML Ops has come onto the scene as an application of the DevOps approach to all aspects of machine learning projects. ML Ops is both a philosophy and a way of organizing human and technical resources.

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Tags big-data machine-learning devops