These Ideas Could be Part of Microsoft Artificial Intelligence Roadmap
Microsoft has been steadily growing into one of the powerhouses of the modern artificial intelligence(AI) and machine learning(ML) ecosystems. With fierce competition from companies such as Amazon, IBM, Google or Faceboof heating up, innovation and rapid iteration are essential for Microsoft to maintain its leadership position in the space.
In this post, I would like to discuss some ideas that might be relevant to the short-term roadmap of Microsoft’s AI-ML technologies. The ideas explored here are a combination of market requirements, capabilities that can fill a gap with the competition as well as natural evolutionary steps for the Microsoft AI-ML stack. Before brainstorming about the roadmap, let’s explore Microsoft’s current AI-ML capabilities.
Microsoft AI-ML Technologies
Microsoft has been actively innovating on diverse areas of the AI-ML ecosystem ranging from cloud cognitive services to deep learning frameworks. The following list summarizes some of the main components of Microsoft’s AI-ML stack:
— Azure Machine Learning: Azure ML is a native cloud service that enables the implementation and management of ML solutions and expose them via APIs. Azure ML provides native support for languages such as R and Python.
— Microsoft Cognitive Services: Microsoft’s Cognitive Services are a series of cloud APIs that provide AI capabilities in cognitive computing areas such as vision, speech, text and knowledge.
— Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit is an open source deep learning framework that supports the implementation of AI application on different languages such as C++ or Python.
— R Server: Microsoft R Server enables the implementation of R-based AI-ML solutions. The newest release adds a new package called Microsoft ML that includes a series of new ML algorithms implemented natively on R.
Some Ideas for Microsoft’s AI-ML Roadmap
Looking at the current state of the market and the current capabilities of Microsoft’s AI-ML technologies, I’ve listed a few ideas that might relevant to Microsoft’s short-term AI-ML roadmap:
— On-Premise Azure ML: Supporting Azure ML as part of Azure Stack will open a window to new scenarios for ML solutions in the enterprise that require on-premise infrastructures. An on-premise version of Azure ML will be a strong competitive advantage with other cloud ML offerings.
— Cognitive Services Training Tools: Microsoft has done a great job opening up the training stack for its Language Understanding Intelligence Service (LUIS). Similar AI training tools for other Microsoft Cognitive Services APIs would be a nice addition to the platform.
— Cognitive Toolking Integration with Azure ML: Just as Google provides integration with its open source deep learning framework TensorFlow and the Google Cloud ML platform, Microsoft should explore a similar strategy to integrate Azure ML and the Cognitive Toolkit framework. That approach will enable the implementation of hybrid deep learning applications for enterprise using the Microsoft stack.
— Azure ML Integration with Deep Learning Frameworks: This might sound controversial but I believe Azure ML would benefit from the integration with some of the top deep learning frameworks in the market such as TensorFlow, Torch, Caffe, Theano, etc. That level of integration will expose Azure ML to a broader set of applications and will serve as a differentiator with the competition.
— C# AI Frameworks: The number of AI-ML frameworks that support C# pales in comparison to other languages such as R or Python. Microsoft recently announced C# support in the Cognitive Toolkit framework but it should expand its effort to provide stronger AI capabilities in its marquee language. That strategy will allow Microsoft to leverage the large C# developer and partner community in the implementation of AI applications and will reduce the challenge of enterprises looking to onboard AI talent.