First published on MSDN on Nov 07, 2018
We'll continue to explore the Azure Data Architecture Guide with our third blog entry in this series. The entries for this blog series are:
- Azure Data Architecture Guide – Blog #1: Introduction
- Azure Data Architecture Guide – Blog #2: On-demand big data analytics
- Azure Data Architecture Guide – Blog #3: Advanced analytics and deep learning (this one)
- Azure Data Architecture Guide – Blog #4: Hybrid data architecture
- Azure Data Architecture Guide – Blog #5: Clickstream analysis
- Azure Data Architecture Guide – Blog #6: Business intelligence
- Azure Data Architecture Guide – Blog #7: Intelligent applications
- Azure Data Architecture Guide – Blog #8: Data warehousing
- Azure Data Architecture Guide – Blog #9: Extract, transform, load (ETL)
Like the previous post, we'll work from a technology implementation seen directly in our customer engagements. The example can help lead you to the ADAG content to make the right technology choices for your business.
Advanced analytics and deep learning
Go beyond historical reporting and exploratory analysis of your data by enabling predictive processing and automated decision making with Azure services like Azure Machine Learning and Apache Spark on HDInsight . When you need to harness the power of multiple GPUs to build sophisticated deep neural architectures and train them on a large data set, get a jump start on the task with Deep Learning Virtual Machines and CNTK , the unified deep-learning toolkit by Microsoft. See also this deep learning sample architecture .
Highlighted services
- Azure Machine Learning
- Deep Learning Virtual Machines
- Apache Spark on HDInsight
- Azure Storage blobs
- HDInsight with Spark
- Azure App Service
Related ADAG articles
- Big data architectures
- Scenarios
- Technology Choices
Please peruse ADAG to find a clear path for you to architect your data solution on Azure:
Azure CAT Guidance
"Hands-on solutions, with our heads in the Cloud!"