You're reading from Machine Learning Engineering on AWS

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Published inOct 2022

PublisherPackt

ISBN-139781803247595

Edition1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1)

Joshua Arvin Lat

Using the Debugger Insights Dashboard

When working on ML requirements, ML practitioners may encounter a variety of issues before coming up with a high-performing ML model. Like software development and programming, building ML models requires a bit of trial and error. Developers generally make use of a variety of debugging tools to help them troubleshoot issues and implementation errors when writing software applications. Similarly, ML practitioners need a way to monitor and debug training jobs when building ML models. Luckily for us, Amazon SageMaker has a capability called SageMaker Debugger that allows us to troubleshoot different issues and bottlenecks when training ML models:

Figure 6.24 – SageMaker Debugger features

The preceding diagram shows the features that are available when we use SageMaker Debugger to monitor, debug, and troubleshoot a variety of issues that affect an ML model’s performance. This includes the data capture capability...

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Machine Learning Engineering on AWS

Published in: Oct 2022Publisher: PacktISBN-13: 9781803247595

Author (1)

Joshua Arvin Lat

Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO for three Australian-owned companies and as director of software development and engineering for multiple e-commerce start-ups in the past. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.
Read more about Joshua Arvin Lat

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