Anthropic State-of-the-Art Models Available to Azure Databricks Customers
Our customers now have greater model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks. Databricks is announcing a partnership with Anthropic to integrate their state-of-the-art models into Databricks Data Intelligence Platform as a native offering, starting with Claude 3.7 Sonnet http://databricks.com/blog/anthropic-claude-37-sonnet-now-natively-available-databricks. With this announcement, Azure customers can use Claude Models directly in Azure Databricks. Foundation model REST API reference - Azure Databricks | Microsoft Learn With Anthropic models available in Azure Databricks, customers can use the Claude "think" tool with business data optimized promote to guide Claude efficiently perform complex tasks. With Claude models in Azure Databricks, enterprises can deliver domain-specific, high quality AI agents more efficiently. As an integrated component of the Azure Databricks Data Intelligence Platform, Anthropic Claude models benefit from comprehensive end-to-end governance and monitoring throughout the entire data and AI lifecycle with Unity Catalog. With Claude models, we remain committed to providing customers with model flexibility. Through the Azure Databricks Data Intelligence Platform, customers can securely connect to any model provider and select the most suitable model for their needs. They can further enhance these models with enterprise data to develop domain-specific, high-quality AI agents, supported by built-in custom evaluation governance across both data and models.Deriving advanced insights with Artificial Intelligence using Azure Machine learning and Snowflake
https://azure.microsoft.com/en-us/services/machine-learning/#product-overview is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows. https://www.snowflake.com Data Cloud supports advanced workloads like Artificial intelligence and Machine learning enabling enterprises to have a single place to instantly access all the relevant data.
Agile Data Vault 2.0 Projects with Azure DevOps
Having discussed the value of Data Vault 2.0 and the associated architectures in the previous articles of this blog series, this article will focus on the organization and successful execution of Data Vault 2.0 projects using Azure DevOps. It will also discuss the differences between standard Scrum, as used in agile software development, and the Data Vault 2.0 methodology, which is based on Scrum but also includes aspects from other methodologies. Other functions of Azure DevOps, for example the deployment of the data analytics platform, will be discussed in subsequent articles of this ongoing blog series.AI/ML ModelOps is a Journey. Get Ready with SAS® Viya® Platform on Azure
Do you want easy answers to following set of questions? Then this article is for you. How many AI/ML models do we have? Where are they stored/inventoried? When was each model updated? By whom? How? Who manages our models? Are the right models being used in production? How do we know? What effort is needed to deploy models? Who’s responsible? Are there documented processes? How long does a model take to be deployed? How old is the data it was trained on? Is the data clean and trustworthy? How are models performing? How do we compare different models for the same use case over time? Does IT work with our analytics teams to create development environments that make it possible to create models that can be easily deployed?
