Glossary
Below is a glossary of key terms and concepts used throughout the Sahara AI User Guide:
A set of protocols and tools that enable developers to interact programmatically with Sahara AI. The API is used for creating, deploying, and managing AI pipelines as well as controlling compute resources.
A core component of the Sahara AI Product Suite responsible for managing compute instances and endpoints. It handles the provisioning of resources needed to run AI workloads and deploy models.
A virtual server or container managed within the Compute Hub that runs AI workloads and hosts deployed AI model endpoints.
Third-party services (such as Lepton, Predibase, Sagemaker, Bedrock, OpenAI) that supply the computational power required to execute AI workloads on the Sahara AI platform.
An optional configuration element in a pipeline that initiates conversation flows, particularly useful in prompt-based pipelines for engaging AI interactions.
A marketplace within the Sahara AI platform where users can discover, purchase, and sell datasets. It serves as a centralized resource for accessing valuable data.
A secure, decentralized storage solution for datasets. Data Vaults ensure data privacy, maintain provenance, and integrate seamlessly with AI pipelines.
A collection of structured data (in formats such as CSV, JSON, or Parquet) used for training or validating AI models. Datasets can be sourced from the Data Hub or uploaded directly by users.
The gateway to the Sahara AI platform that integrates all services into a unified interface. It provides developers with the tools needed to build, manage, and deploy AI assets.
A centralized interface for browsing, creating, and deploying AI pipelines. The Model Hub supports both Retrieval-Augmented Generation (RAG) and prompt-based pipelines, making it easier to manage AI models.
A sequence of interconnected processes designed to automate the flow of data and model interactions. Pipelines in the Model Hub guide users through the steps of configuring and deploying AI workflows.
A type of AI pipeline that uses prompt engineering to handle conversational or structured tasks. It relies on input prompts to drive the behavior and responses of the AI model.
Retrieval-Augmented Generation (RAG)
A pipeline method that integrates data retrieval from secure Data Vaults with AI generation processes. This approach enhances the relevance and accuracy of the AI-generated responses.
Statistical data that track performance and consumption of platform resources, including API calls, token usage, and overall system performance.
A feature that allows users to manually upload their own datasets into a vault. Supported file formats include CSV, JSON, and Parquet, enabling the integration of custom data into AI pipelines.
A structured sequence of processes within the Sahara AI platform that guides users from initial data upload through to pipeline creation, deployment, and monitoring.
This glossary provides quick definitions to help users navigate the Sahara AI Product Suite and understand the terminology used throughout the user guide.
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