Building and Automating ML Pipelines with Amazon SageMaker Studio

Success in machine learning is all about streamlining the entire workflow. Automation is critical in accelerating development, ensuring consistency, and enabling scalable experimentation. Amazon SageMaker Studio, an integrated development environment (IDE) for machine learning, empowers data scientists and engineers to build, train, and deploy ML models with minimal friction while automating complex workflows.

In this Cloud Lab, you’ll create an automated machine learning pipeline with an architecture similar to the one provided below:

As shown above, you will create an S3 bucket, add a dataset, and create the necessary IAM roles for Amazon SageMaker Studio operations. You will create a domain and a user in Amazon SageMaker AI. After that, you will also create a machine learning pipeline in it that will be able to do data processing, model training, and then model deployment. Moreover, you will automate the execution of the machine learning pipeline whenever a new dataset is uploaded to the S3 bucket with the help of Lambda function triggers. In the end, you will also create a Lambda function to invoke the endpoint of the Sagemaker model to get results from it.