Gradient Group – AWS Machine Learning Blog

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

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

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

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