First published on MSDN on Dec 07, 2018
We'll continue to delve into the Azure Data Architecture Guide with our seventh 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
- 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 (this one)
- 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.
Intelligent applications
Quickly add intelligence to your applications with Cognitive Services, and coordinate automated interactions using Azure Bot Service. This can save you months of creating and refining sophisticated algorithms to naturally interact with your users through speech, text, vision, knowledge, and search capabilities.
Highlighted services
- Azure App Service
- Azure SQL Database
- Azure Bot Service
- Custom Speech Service
- Azure Cognitive Services
- Azure Active Directory B2C
Related ADAG articles
- Traditional RDBMS workloads
- 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!"