# Streamlining Machine learning and Data Science development with Amazon SageMaker spaces

Amazon SageMaker Spaces is a feature that allows users to collaborate on machine learning projects in a secure and isolated environment. With SageMaker Spaces, users can create shared workspaces where they can share notebooks, files, experiments, and models with other users. Users can also control access to these resources, so they can be sure that their data is safe.

SageMaker Spaces is a great way to accelerate the machine learning development process. By sharing resources with other users, users can get feedback on their work quickly and easily. Users can also collaborate on experiments and models, which can help them to improve the quality of their results.

To get started with SageMaker Spaces, users first need to create a domain. A domain is a collection of shared workspaces. Once users have created a domain, they can create shared workspaces within it. To create a shared workspace, users need to specify a name for the workspace and a list of users who will have access to it.

Once users have created a shared workspace, they can start sharing resources with other users. To share a notebook, file, experiment, or model, users need to select the resource and then click the "Share" button. In the "Share with" dialog box, users can select the users who they want to share the resource with.

Users can also control access to resources by using IAM roles. When users create a shared workspace, they can specify an IAM role that will be used by default for all resources in the workspace. Users can also specify individual IAM roles for specific resources.

SageMaker Spaces is a powerful new feature that can help users to collaborate on machine learning projects more effectively. By sharing resources with other users, users can get feedback on their work quickly and easily. Users can also collaborate on experiments and models, which can help them to improve the quality of their results.

Here are some of the benefits of using Amazon SageMaker Spaces:
* Collaboration: SageMaker Spaces makes it easy for users to collaborate on machine learning projects. Users can share notebooks, files, experiments, and models with other users in a secure and isolated environment.
* Security: SageMaker Spaces provides a high level of security for user data. Users can control who has access to their resources and they can revoke access at any time.
* Scalability: SageMaker Spaces is designed to scale to meet the needs of the user's team. Users can create as many shared workspaces as they need and they can add users to these workspaces as needed.
* Cost-effectiveness: SageMaker Spaces is a cost-effective way for users to collaborate on machine learning projects. Users only pay for the resources that they use.
If users are looking for a way to collaborate on machine learning projects, then Amazon SageMaker Spaces is a great option. It is secure, scalable, and cost-effective.


This workshop consists of following modules:
 - [AWS CodeCommit version control](./SETTING_UP.md#aws-codecommit-version-control)
 - [IAM Policy to access the CodeCommit repository](./SETTING_UP.md#iam-policy-to-access-the-codecommit-repository)
 - [SageMaker Spaces](./SETTING_UP.md#sagemaker-spaces)
 - [Conclusion](./SETTING_UP.md#conclusion)
 - [References](./SETTING_UP.md#references)


## Getting Started
[Setting up the environment and loading the notebooks in Sagemaker spaces](./SETTING_UP.md)

## Security

See [CONTRIBUTING](../CONTRIBUTING.md#security-issue-notifications) for more information.

## License

This library is licensed under the MIT-0 License. See the LICENSE file.