Sahara Documentation
User Guide: AI Developer Platform
User Guide: AI Developer Platform
  • Dataset Registry & Tokenization
  • Troubleshooting
  • API Documentation
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On this page
  • Step 1: Adding Data to Vaults
  • Option A: From the Data Hub
  • Option B: Upload Personal Data
  • Step 2: Setting Up an Endpoint in the Compute Hub
  • Step 3: Creating and Deploying Pipelines
  • A. Create a Pipeline in Model Hub
  • B. Deploy the Pipeline
  • Step 4: Integration and Monitoring
  • A. Test the Endpoint
  • B. Monitor Metrics

Workflow Steps

Last updated 27 days ago

Step 1: Adding Data to Vaults

Option A: From the Data Hub

  1. Browse Data Hub:

  • Navigate to the Data Hub within the Sahara platform.

  • Explore available datasets by filtering by domain, size, or licensing terms. (Some explore features coming in Beta)

  1. Purchase Dataset:

  • Select a dataset and click Purchase.

  • Complete the payment process using the supported token or currency.

  1. Add Dataset to Vault:

  • A Screen will pop up and you will select the Vault you want to import to and click Import.

  • Navigate to your Vaults Tab and see the imported data set.

Option B: Upload Personal Data

  1. Navigate to Vaults:

  • Go to the Vaults Tab in the Sahara dashboard.

  1. Create a New Vault:

  • Click Create Vault and provide a name and description.

  1. Upload Data:

  • Click Upload Files and select files from your local storage.

  • Supported formats include CSV, JSON, and Parquet.

  1. Configure Metadata:

  • Add metadata tags for better discoverability and provenance tracking.

Step 2: Setting Up an Endpoint in the Compute Hub

  1. Access My Submissions:

  • Open the My Submissions on the Sahara dashboard after you purchase a model.

  1. Initiate:

  • Click the New Endpoint button on the My Submissions home page.

  1. Configuration:

  • Fill out the form with the following details:

  • Select Provider: Select Provider from list (Lepton, Predibase, Sagemaker, Bedrock, OpenAI)

  • Select Model: Opens a popup and allows users to select from available models on platform. Search and press “Select” when you have chosen a model.

  • Name: Assign a unique name to the instance.

  1. Review and Create:

  • Review the configuration.

  • Click Create Instance to launch the instance.

Step 3: Creating and Deploying Pipelines

A. Create a Pipeline in Model Hub

  1. Open Model Hub:

  • Navigate to the Model Hub Tab.

  1. Click Create Pipeline:

  • Choose between:

  • RAG Pipeline: Requires Vault integration for retrieval tasks.

  • Prompt-Based Pipeline: For conversational or structured AI tasks.

  1. Configure Pipeline:

  • Upload an avatar for the pipeline.

  • Provide the following:

  • Pipeline Name.

  • Description.

  • Instructions (prompt).

  • Conversation Starter (optional).

  • Select an AI model.

  • (RAG only) Choose Vaults for data retrieval.

B. Deploy the Pipeline

  1. Publish Pipeline:

  • Click Publish and select a compute provider matching your pipeline’s requirements.

  1. Generate API Endpoint:

  • Upon deployment, an API endpoint is generated for integration.

Step 4: Integration and Monitoring

A. Test the Endpoint

  1. Test the API:

  • Use the generated endpoint to send sample requests.

Example payload:

  • Review the AI response and refine configurations as needed.

B. Monitor Metrics

  1. Open Dashboard Tab:

  • Navigate to the Metrics Tab in your pipeline’s details page.

  1. Review Usage:

  • Monitor token usage, API calls, and system performance metrics.