Get started
Learn how to deploy and use the AI Unlimited AI/ML engine in the cloud.
With AI Unlimited, data scientists and data engineers can explore and analyze large datasets in a Jupyter notebook using x-many ClearScape AnalyticsTM functions—on a self-service, on-demand basis.
(make TM smaller)
(need to discuss doc link w/team - branding? - is the whole doc applicable?)
(it will actually go to the lastest published doc--be it the Lake version or the 17.20 version - but we can make it always go to one or the other)
How it works
You connect your notebook to the AI/ML engine on AWS or Azure, and connect the engine to your Amazon S3 or ADLS Gen2 data lake. You can suspend and resume your analytics project anytime, and pay only for the hours you use.
Included in AI Unlimited
- An AI/ML engine that you deploy on AWS or Azure
- A manager, which orchestrates the engine's deployment—and includes a web-based user interface for monitoring projects
- The AI Unlimited Jupyter Kernel for managing projects in notebooks
Prerequisites
- A pay-as-you-go AWS or Azure account (see requirements) (link to content in "Other resources") on which to provision compute resources
- A GitHub or GitLab account (see requirements) (link to content in "Other resources") to host your repository for authenticating users and storing project information
- Your object storage, where your Amazon or ADLS Gen2 data lake resides
- JupyterLab
You might like to [get deployment details](link to them in "Other resources") ahead of time. You'll need them to deploy the cloud template for the manager.
With AI Unlimited you can collaborate on projects with other users.
If you prefer a simpler, single-user approach, try the QuickStart which runs the manager and JupyterLab in Docker containers on your computer.
Subscribe
Subscribe to AI Unlimited on the [AWS Marketplace] (link to listing) or [Azure Marketplace] (link to listing).
What's next
Return to this documentation site to install the manager on AWS or Azure.