AI in the Cloud Adoption Framework
Review a prescriptive framework that includes the tools, programs, and content (best practices, configuration templates, and architecture guidance) to simplify adoption of AI and cloud-native practices at scale.
The list of required actions is categorized by persona to drive a successful deployment of AI in applications, from proof of concept to production, then scaling and optimization.
To prepare for this phase of the cloud adoption lifecycle, use the following exercises:
- Machine learning model development, deployment, and management: Examine patterns and practices of building your own machine learning models, including machine learning operations (MLOps), automated machine learning (AutoML), and Responsible ML learning tools such as InterpretML and FairLearn.
- Adding domain-specific AI models to your applications: Learn about best practices for adding AI capabilities into your applications with Cognitive Services. Also learn about the key scenarios these services help you address.
- Build your own conversational AI solution: Learn how to build your own Virtual Assistant, a conversational AI application that can understand language and speech, perceive vast amounts of information, and respond intelligently.
- Build AI-driven knowledge mining solutions: Learn how to use knowledge mining to extract structured data from your unstructured content and discover actionable information across your organization's data.