Full-service platform for building, training, and deploying machine learning models.
AWS SageMaker
Overview
Go Deeper
Overview
In 1 sentence, what does this tool do?
The tool helps to build, train, and deploy machine learning models for any use case with fully managed infrastructure, tools, and workflows.
What problems does it solve?
Simplifies the machine learning lifecycle, from model development to deployment, ensuring efficient infrastructure management.
Go Deeper
How does this tool help your team?
SageMaker is used by the team to launch new machine learning training jobs easily through its Studio workspace. Once the model is trained, evaluated, and accepted, the team members can check-in their code in Git quickly and create an endpoint to deploy the model.
What do you like/ dislike about this tool?
The best part is that there is no need to port the code or struggle with environmental dependencies in the entire process. The cost of training a ML model is in control of the development team.
What tips do you have for beginner users?
Use the JumpStart feature to quickly install any LLM or open-source model and start your training and evaluation job instantly.
Usage
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