Certifications

AWS Certified AI Practitioner

With Databricks Feature Serving, you can serve structured data for retrieval augmented generation (RAG) applications, as well as features that are required for other applications, such as models served outside of Databricks or any other application that requires features based on data in Unity Catalog.

Using the Foundation Model APIs you can: Query a generalized LLM to verify a project’s validity before investing more resources. Query a generalized LLM in order to create a quick proof-of-concept for an LLM-based application before investing in training and deploying a custom model. Use a foundation model, along with a vector database, to build a chatbot using retrieval augmented generation (RAG). Replace proprietary models with open alternatives to optimize for cost and performance. Efficiently compare LLMs to see which is the best candidate for your use case, or swap a production model with a better performing one. Build an LLM application for development or production on top of a scalable, SLA-backed LLM serving solution that can support your production traffic spikes.