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[Book Discussion] - Model Management in the Real World

Discussion created by maprcommunity Employee on Sep 26, 2017
Latest reply on Dec 4, 2017 by slimbaltagi

            ABOUT THIS BOOK 

   Machine Learning Logistics

   How do you get a machine learning system to deliver value from    big data?
   Turns out that 90% of the effort required for success in machine    learning is not the algorithm or the model or the learning - it's the    logistics. Ted Dunning and Ellen Friedman identify what matters in    machine learning logistics, what challenges arise, especially in a    production setting, and they introduce an innovative solution: the    rendezvous architecture. This new design for model management      is based on a streaming approach in a microservices style.                  Rendezvous addresses the need to preserve and share raw data,      to do effective model-to-model comparisons and to have new              models on standby, ready for a hot hand-off when a production model needs to be replaced.

 

In this book, you’ll learn:

  • Why successful machine learning projects involve many models
  • How to use a decoy model to capture exact data inputs
  • The capabilities needed in stream transport technology to support a stream-first microservice approach
  • How to do accurate model evaluation, including use of the t-Digest
  • Why a canary model is useful in a production setting
  • How to achieve rapid and seamless deployment of new models
  • The role of DataOps style of work and a global data fabric in making logistics for machine learning much easier

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Add a comment below. What did you think about this book?, What was your favorite part?, What would you like to read next about?

 

Haven't read the book yet?, find it here:  Machine Learning Logistics: Model Management in the Real World | MapR 

See more available books in the  Converge Book Club 

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