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Measures and indicators for machine learning maturity

Every year we speak with hundreds of companies aiming to incorporate machine learning (ML) into their organization in order to automate and accelerate specific business functions. At Algorithmia, we are interested in how ML is spreading across the enterprise, specifically what challenges organizations face that prevent or hinder their ML value extraction.

Building and training an ML model is not the end goal for the enterprise. In this ebook, we’ll explore the ways an organization can achieve ultimate ML success quickly and how it should measure and maintain an ongoing machine learning program.  

In this ebook, you will:

Measures and indicators for machine learning maturity

Every year we speak with hundreds of companies aiming to incorporate machine learning (ML) into their organization in order to automate and accelerate specific business functions. At Algorithmia, we are interested in how ML is spreading across the enterprise, specifically what challenges organizations face that prevent or hinder their ML value extraction.

Building and training an ML model is not the end goal for the enterprise. In this ebook, we’ll explore the ways an organization can achieve ultimate ML success quickly and how it should measure and maintain an ongoing machine learning program.  

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 In this ebook, you will:

  • Learn the five key measures of operating and maintaining a healthy ML program
  • Discover the five key performance indicators of these ML program measures
  • Learn why organizations need a robust ML operations and management framework