AWS SageMaker is a fully-managed service for machine learning in the cloud. It lets us build and train ML models, and directly deploy them into a production-ready, hosted environment. Its various features include:
- An integrated Jupyter notebook authoring instance. It offers easy access to data sources for exploration and analysis. There is no need to manage servers.
- Common machine learning algorithms optimized to run against large data in a distributed environment. SM also lets you use your own frameworks and algorithms, offering flexible distributed training capabilities that can adjust to specific workflows.
I prefer SM console over Studio
WORK FLOW
In SM, ML modeling process comprises 1. preparation, 2. training, and 3. deployment. Following diagram illustrates typical workflow.
1. PREPARATION
SageMaker creates a fully-managed ML instance in Amazon EC2 and runs Jupyter computational processing notebooks.
