Amazon SageMaker JumpStart is a machine learning (ML) platform that offers pre-built solutions and pre-trained models. The platform provides access to hundreds of foundation models (FMs) for various enterprise operations. A critical feature of SageMaker JumpStart is the private hub, which allows an organization to share their models, thereby facilitating the discovery and widespread use of ML models within the enterprise.
Private hubs assist enterprise administrators in controlling which FMs are available to users in their organization. The benefits are that administrators can allow only vetted models to be accessed, ensuring consistency, adherence to regulations, and enhancing security within the organization.
The private hub feature facilitates the creation of repositories for different models specific to teams, use cases, and licensing requirements. Administrators have the option of setting up multiple private hubs, each containing a custom set of models to be discovered by different groups of users.
SageMaker JumpStart and its private hub feature provide enterprises the convenience of accessing the newest open-source generative artificial intelligence (AI) models while maintaining control and governance. Furthermore, cross-account sharing of private hubs fosters collaboration across different teams or departments within an organization, even when they operate under different AWS accounts.
Users within an organization can access and use models within the private hubs they have access to via Amazon SageMaker Studio and the SDK. The AWS Resource Access Manager (RAM) security protocol is used to safely share private hubs with other accounts within the same organization.
Overall, the SageMaker JumpStart and its private hub feature support the strategic deployment of AI and ML within an organization while mitigating risks associated with unvetted models.
Besides, organizations can tailor the AI and ML systems to align with their specific needs, objectives, and regulatory requirements. The private hub facilitates the decoupling of model curation from model consumption, allowing administrators to manage the model inventory while data scientists focus on developing AI solutions.
The private hub supports robust model governance across an entire organization, and its scalability meets enterprise-level ML demands. It also integrates with AWS RAM to securely share curated model repositories, thereby promoting cross-functional collaboration and consistent model governance.
