Machine learning (ML) workflows orchestrate and automate sequences of ML tasks by enabling data collection and transformation. This is followed by training, testing, and evaluating a ML model to achieve an outcome. For example, you might want to perform a query in Amazon Athena or aggregate and prepare data in AWS Glue before you train a model on Amazon SageMaker and deploy the model to production environment to make inference calls. Automating these tasks and orchestrating them across multiple services helps build repeatable, reproducible ML workflows. These workflows can be shared between data engineers and data scientists.
This solution is a modified version of this blog post https://aws.amazon.com/blogs/machine-learning/build-end-to-end-machine-learning-workflows-with-amazon-sagemaker-and-apache-airflow/ Thanks!