AI resources for blending Microsoft AI Data Science into your curricula
Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. At Microsoft, we have developed two sets of Boot Camp materials
This curriculum is primarily oriented towards these two personas which meet the demands of Undergraduate and Postgraduate students.
1. Emerging AI developer
The emerging AI developer. The target profile here is developer who is yet to use Microsoft AI tools and APIs to infuse intelligence into their applications. the course content also suits more advanced AI developers. This profile relates to developers and data scientists who currently build AI and machine learning solutions and want to know how to do this with Microsoft’s tools, framework and processes, such as the Azure Machine Learning Workbench and the Team Data Science Process.
Services covered include, Cognitive Services and Azure Bot Services. covering aspects of the Microsoft Azure Cognitive Services, and then deep dive into our Vision APIs, specifically, Computer Vision and Custom Vision. With Computer Vision, we store descriptions and tags from various images in Cosmos DB, Microsoft’s globally distributed, multi-model database, Azure Search index on top of it, and create a bot with NLP capabilities using LUIS (our language understanding service) to greet, query, and share images. and finally set up logging for a bot and explore different techniques for testing the application.
2. Professional AI developer
Professional AI developers, use the Azure Machine Learning Workbench to develop, test and deploy ML solutions using an agile and team-oriented framework. The Workbench uses Python with Conda environments to handle package dependencies, and Docker to create, test and deploy ML solutions which can then be served as applications using Azure Container Services. the content also cover how the Workbench syncs with Azure Machine Learning Services to store and manage ML models and related assets, as well as the services created by them.
So if your interested in blending these resources into your curricula all the materials are available on GitHub at the links below.
Go ahead and try these out at your own convenience and do share your feedback and thoughts with us on how your building this material into your academic curricula.
Microsoft Machine Learning https://blogs.technet.microsoft.com/machinelearning