Amazon SageMaker AI
by Amazon Web Services
AWS service for building, training and deploying ML models; its API lists training jobs, models, endpoints and pipelines.
- Category
- Cloud & Data Platforms
- Type
- MLOps
- Interface
- REST API
- Built with
- Connect Anything
- Credentials
- AWS access keys
02Data
What you can sync.
- Training jobs
- Models
- Endpoints
- Pipeline executions
Put Amazon SageMaker AI training jobs next to revenue and usage, so every cost has an owner and a reason.
Need more? Name any other Amazon SageMaker AI data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- AWS access keys
Amazon SageMaker AI uses AWS access keys. You add the credentials once, when the AI asks for them, and it tells you where to create them in Amazon Web Services if you need help.
04How it works
How it works in DataMind.
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Pick Amazon SageMaker AI.
Open Lake, pick Amazon SageMaker AI in the source picker and name the connection. Connect Anything opens with a ready prompt that points the AI at the Amazon SageMaker AI API docs.
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Answer a few questions.
The AI reads the Amazon SageMaker AI documentation, then asks which credentials you will use, which data to sync (for example training jobs and models), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
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Your data lands in Lake.
The connector agent writes a Python connector for Amazon SageMaker AI, tests it against the live API and shows you sample rows. Then the data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
