Gradient Group – AWS Machine Learning Blog

Get the latest tactics and strategies to build, test, deploy, and scale your AWS machine learning solutions, models, and end-to-end pipelines

  • Infrastructure-as-Code for Machine Learning Pipelines in AWS

    July 15, 2021 by

    We all start our AWS journey in the console. We do everything there. We manually create and configure Lambda functions, Step Functions, IAM roles, S3 buckets, EMR clusters, and any other service we need as we implement a machine learning solution. We use source control for occasional commits to the dev branch to keep track… Read more

  • Drift Monitoring for Machine Learning Models in AWS

    July 10, 2021 by

    We have trained a machine learning model that meets or exceeds performance metrics as a function of business requirements. We have deployed this model to production after converting our Jupyter notebook into a scalable end-to-end training pipeline, including CI/CD and infrastructure-as-code. This deployment could be a SageMaker endpoint for live inference, or a Lambda function… Read more

  • AWS Cross-Account Deployments for Production ML Pipelines

    September 18, 2021 by

    How do you deploy a machine learning training pipeline as a CloudFormation stack from a dev AWS account to a prod AWS account? Suppose you added feature engineering steps to a component of your machine learning training pipeline (within your development environment). If you are using CodeCommit, CodePipeline, and CodeBuild for CI/CD, follow these steps… Read more

  • CI/CD Pipelines for Machine Learning Solutions in AWS

    July 20, 2021 by

    All machine learning projects within AWS begin in a development environment – usually SageMaker Studio notebooks, or Glue notebooks (when a cluster is needed during PySpark development). Both environments support CodeCommit for source control. Suppose we developed an end-to-end data science workflow in a Jupyter notebook using a dataset extracted from S3, Redshift, Aurora, or… Read more

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