Building Production Machine Learning Systems on Google Cloud Platform

Building Production Machine Learning Systems on Google Cloud Platform

In this article, we will continue the exploration of production machine learning systems on GCP with a special focus on the design of high-performance ML systems.

This is the third part of a four-part series. I suggest you read part 1 and 2 for a better understanding:

Part 1

Part 2

In this article, we will continue the exploration of production machine learning systems on GCP with a special focus on the design of high-performance ML systems.

Content

  • Efficiency at training
  • Fast input pipelines
  • Efficient inferencing

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would love to get fully hands-on before then, I suggest you take the [Advance Machine Learning with Tensorflow on GCP_](https://www.coursera.org/programs/697b2a08-db87-4463-a112-e1ac8c46b181?collectionId=&productId=GWIdT4bQEeiFWQrjbTVkyg&productType=s12n&showMiniModal=true) course by Google ML team for a start._

Design High-Performance ML systems

High-performance ML systems could mean different things to different companies depending on the project goal. This could mean a powerful ML system that has the ability to handle a large dataset, a system that can do the job as fast as possible, a system that has the ability to train for a long period of time, or even achieving the best possible accuracy, etc. These many factors and characteristics of high-performance ML systems are important, but one key aspect is the time it takes to train a model. Assuming we wish to train a model to attain a specific evaluation measure (e.g. accuracy), we could design a high-performance ML system from the infrastructure performance perspective. When allocating or provisioning for infrastructure to the machine learning tasks, we should consider some key factors such as time-to-train, budget, inference, and prediction time.

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