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You're reading from  Machine Learning Engineering with MLflow

Product typeBook
Published inAug 2021
PublisherPackt
ISBN-139781800560796
Edition1st Edition
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Natu Lauchande
Natu Lauchande
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Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
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Making a cloud deployment with AWS SageMaker

In the last few years, services such as AWS SageMaker have been gaining ground as an engine to run ML workloads. MLflow provides integrations and easy-to-use commands to deploy your model into the SageMaker infrastructure. The execution of this section will take several minutes (5 to 10 minutes depending on your connection) due to the need to build large Docker images and push the images to the Docker Registry.

The following is a list of some critical prerequisites for you to follow along:

  • The AWS CLI configured locally with a default profile (for more details, you can look at https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
  • AWS access in the account to SageMaker and its dependencies.
  • AWS access in the account to push to Amazon Elastic Container Registry (ECR) service.
  • Your MLflow server needs to be running as mentioned in the first Starting up a local model registry section.

To deploy...

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Machine Learning Engineering with MLflow
Published in: Aug 2021Publisher: PacktISBN-13: 9781800560796

Author (1)

author image
Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
Read more about Natu Lauchande