Databricks architecture overview | Databricks on AWS
Get a high-level overview of Databricks architecture, including its enterprise architecture in combination with a cloud provider.
Download lakehouse reference architectures | Databricks on AWS
For DWH and BI use cases, the Databricks lakehouse provides Databricks SQL, the data warehouse powered by SQL warehouses, and serverless SQL warehouses. For machine learning, model serving is a scalable, real-time, enterprise-grade model serving capability hosted in the Databricks control plane.
Disaster recovery | Databricks on AWS
Disaster recovery involves a set of policies, tools, and procedures that enable the recovery or continuation of vital technology infrastructure and systems following a natural or human-induced disaster.
AI and machine learning on Databricks | Databricks on AWS
The diagram shows how various products on Databricks platform help you implement your end to end workflows to build and deploy AI and ML systems. In this article: Generative AI on Databricks. Machine learning on Databricks. Deep learning on Databricks. Next steps.
What are Databricks Asset Bundles? | Databricks on AWS
Databricks Assets Bundles are an infrastructure-as-code (IaC) approach to managing your Databricks projects. Use them when you want to manage complex projects where multiple contributors and automation are essential, and continuous integration and deployment (CI/CD) are a …
What is Databricks? | Databricks on AWS
The Databricks platform architecture comprises two primary parts: The infrastructure used by Databricks to deploy, configure, and manage the platform and services. The customer-owned infrastructure managed in collaboration by Databricks and your company.
MLOps workflows on Databricks
Learn the recommended Databricks MLOps workflow to optimize performance and efficiency of your machine learning production systems.
LLMOps workflows on Databricks | Databricks on AWS
Learn the recommended Databricks LLMOps workflow to optimize performance and efficiency of your machine learning production systems.
MLOps Stacks: model development process as code - Databricks
Production engineers configure ML infrastructure that allows data scientists to develop, test, and deploy ML pipelines and models to production. As shown in the diagram, the default MLOps Stack includes the following three components: ML code.
RDBMS (structured) Auto loader. Delta Live Spark / Tables Photon. Databricks SQL. Files / Logs (semi-structured) Assistant.
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