A linear model is a sum of weighted variables that predict a target output value
As a Solutions Architect with focus on Data and AI I have been focusing on my learning goals mostly on Azure Machine Learning, and learning Python programming language.
Azure Machine Learning is a great set of tools to develop Machine Learning Models either by code, with the designer or Automated ML.
When talking about compute, Azure ML has a lot of options to choose from, from CPU/GPU Options to attached vms, etc
One of those attached compute options is Azure Databricks which basically will allow us to run our notebooks and ml processes in an Azure Databricks cluster instead of the default Azure ML compute options.
But what's Azure Databricks?
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure.
1.Designed in collaboration with the founders of Apache Spark
2.One-click set up; streamlined workflows
3.Interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
4.Native integration with Azure services (Power BI, SQL DW, Cosmos DB, Blob Storage)
5.Enterprise-grade Azure security (Active Directory integration, compliance, enterprise -grade SLAs)
In more general terms, Azure Databricks is the entire Spark set of tools running on Azure Cloud.
By providing an optimized, easy to provision and configure environment, Azure Databricks gives developers a performant, cost-effective platform that enables them to spend more time building applications, and less time focused on managing clusters and infrastructure.
I will continue creating content focused on Azure Machine Learning, but some of those future posts or videos will also include Azure Databricks content.
Keep an eye on our future content in our YouTube channel: