Winning developers and the trust of enterprises are two of the biggest arenas of competition between the major cloud providers today and for this reason, every Build for the last six years has been a determining event for Microsoft.
Build 2019 was no different as a steady stream of news focused on many of the key technologies (and buzzwords) that have become vital to developers: cloud, open source, serverless, edge computing, blockchain and mixed reality, to name a few.
But at the epicenter of everything was artificial intelligence (AI), which has become fundamental to Microsoft’s positioning with developers, in its post PC and Windows-centric era. AI was at the heart of the future scenarios for human-machine interaction CEO Satya Nadella showcased during his keynote as well as most of the event's major announcements. Let’s take a closer look at the highlights and what they mean for Microsoft’s strategy and the market moving forward.
The Keynote: AI Underlies Microsoft’s Developer and Cloud Strategy
Nadella took the opportunity of his keynote to frame Microsoft’s strategy, which, unlike previous years, is currently being delivered with unprecedented clarity and precision. The strategy's four main platforms — Azure, Microsoft 365, Dynamics 365 (which includes Power Platform) and Gaming (which wasn’t covered at the event) — provide ample opportunity for developers to build products around as well as have an enviable flywheel effect across Microsoft’s core cloud businesses. In my view, these pillars are a great way to look at the entirety of Microsoft’s cloud proposition today.
A key enabler spanning this picture is AI, as shown in the Microsoft image below. Microsoft focuses on three main audiences for AI: developers, organizations and industries, and people. The firm’s capabilities can also be broken down between three categories, based on customer maturity:
- Incubation — Aimed at helping firms bring AI to every application through its Azure developer tools spanning infrastructure, platform and developer services.
- Transformation — Which orients around bringing AI to every business process and concentrates on its packaged business solutions in Azure as well as the Dynamics and Power Platforms.
- Productivity — Which is about bringing AI to every employee through the infused AI in Microsoft 365.
So too, for the first time, did students.
In a clever move ahead of Build, Microsoft announced attendees could bring their children to the event. The timing coincided with the final leg of its annual student developer competition, the Imagine Cup, which Microsoft held just before the opening keynote. An 18-year-old freshman from UCLA won for his work developing a smartphone-based blood glucose monitor using Azure Machine Learning aimed at tackling the global diabetes epidemic. It was an inspiring showcase of Microsoft’s technology and the keynote’s "new and better world" message.
A New Vision for Conversational AI, Cortana and Intelligent Agents
The Build keynote also saw Nadella articulate Microsoft’s renewed vision for conversational AI and intelligent agents. This area is growing in prominence as Microsoft expands its collection assets such as its Bot Framework, the incorporation of its XOXCO and Semantic Machines acquisitions last year, Cognitive Services, its Xiaoice social chatbot in Asia and, above all, Cortana. These assets have been growing in popularity with developers looking to build bots, which often represents their first experience in building AI.
Microsoft stated that more than 1.2 million developers are using its AI tools today, with 350,000 of them using its Bot Framework to create on average nearly 3,000 bots per week, including BMW, which is putting Microsoft’s speech AI into the next generation of its cars.
Build showed how Microsoft, by bringing together Microsoft Research with the expertise of Semantic Machines, is quickly developing new conversational interfaces that support multi-turn dialogue, multiple skills domains and multiple third-party agents to deliver richer, more context-aware interactions. A highlight of the keynote was a video which showed a more natural, functional and conversational Cortana being used as a workplace assistant, scheduling calendar appointments, sending driving directions to a car and booking tables at a restaurant. It led some to question how real the technology was, but following a private demonstration of the technology at the event by University of California Berkeley professor and Semantic Machines co-founder Dan Klein, I can confirm the progress Microsoft is making here.
Microsoft has been feeling pressure to show progress around Cortana over the past year as it continues to lag behind the competition. According to our most recent survey of employee preferences for example, Cortana landed in fourth place behind Google Assistant, Apple Siri and Amazon Alexa.
Though speech assistants have a long way to go before they gain widespread acceptance within enterprises, Microsoft must continue to invest in Cortana. The platform will likely become the next user interface and front end to the Microsoft 365 experience and above all, Cortana must counter the early lead Google has in this area with Google Assistant, which is now integrating into G Suite, Microsoft’s fiercest rival in cloud productivity.
Related Article:Workplace Showdown: Amazon Alexa vs. Google Assistant
Azure Cognitive Services Suite Expands With New Features
A big investment area that came alive at Build was the Microsoft Azure Cognitive Services set of developer APIs. The company announced several enhancements to its portfolio including a new category, Decision, which incorporates two existing services, Content Moderator and Anomaly Detector, as well as a new service called Personalizer which uses reinforcement learning to deliver a relevant experience to each user. Additional new features include Ink Recognizer, which can read handwriting, Forms Recognizer, which extracts data from forms to help automate data entry, and real-time conversation transcription capabilities.
Microsoft also announced the general availability of Azure Cognitive Search, which integrates with Azure Cognitive Services to augment data as it’s ingested, enhancing content understanding and creating a richer search index.
Cognitive Services are evolving into a premier collection of developer APIs. However, a criticism of the cloud vendors in this area has been that they lack domain relevance for businesses and few focus on improving business processes or solving more industry-specific problems. Microsoft should leverage its Azure AI Accelerators and Azure AI Gallery here to release solutions for businesses such as fraud detection, compliance monitoring, predictive maintenance and dynamic pricing. We expect more on this over the next 12 months.
Azure Machine Learning Pushes into MLOps
Microsoft also announced a series of important enhancements to its Azure Machine Learning platform. These included:
- A new, no-code visual interface for its automated machine learning service. This is geared at improving data scientist productivity by automating the testing of multiple models in parallel.
- High-speed inferencing from cloud to edge with the general availability of hardware-accelerated models that run on FGPAs in Azure and in preview, Data Box edge. It also announced that Project Brainwave entered preview and that it now offers ONNX Runtime support for NVIDIA TensorRT and Intel nGraph.
- A machine teaching platform based on reinforcement learning. This enables domain experts inside companies with limited AI experience to train computers and create autonomous systems for business and industrial scenarios such as smart buildings, industrial machinery and robotics. The technology is based on the Bonsai acquisition Microsoft made last year.
Arguably the most important announcement in this area however was the release of MLOps, a devops environment for machine learning. Integrated with Azure DevOps as well as GitHub, MLOps is an end-to-end lifecycle management solution for machine learning operations, with capabilities to improve processes for model creation, deployment and most crucially, performance in production environments. It also offers many advantages in terms of model reproduction, auditing, validation and observation.
In our view, its most important benefit however is that it helps businesses improve the governance of machine learning through features such as tracking bias in data pipelines, explainability in model outcomes, monitoring data drift and assessing overall model performance and quality assurance. It does this through continuous monitoring of the feedback loop process that helps firms improve algorithmic quality and run-state operations. These areas have become critical as businesses look to move AI projects from experimentation to operationalization in their organizations.
Related Article: Cloud Computing Takes a Back Seat to Edge Computing. Or Is it Fog?
Key Build '19 Takeaways
Altogether, Build’s AI announcements reveal a few important takeaways. First, Microsoft is clearly demonstrating some strength with enterprise developers, especially in the areas of speech and bots. Also evident were early signs of success from Microsoft’s 2018 acquisitions in this area, namely Semantic Machines and XOXCO. While work still needs to be done to consolidate all of its properties in this area for a clearer go to market strategy, including incorporating the Xiaoice technology, which now has 660 million users, into other areas, Microsoft is beginning to show market leadership in this area.
Second, with the expansion of its Azure Cognitive Services, the incorporation of “everyday AI” features in Microsoft 365 and the improvement of its inference and edge deployment capabilities, Microsoft continues to increase the breadth of scenarios for its AI. In our view, this scope is why Microsoft was selected as the top brand for advancing AI in the enterprise, ahead of Google, AWS and IBM according to our recent survey of IT decision makers.
Finally, the Azure AI platform, which includes Azure Machine Learning tools, Azure AI infrastructure and Azure Cognitive Services, continues to expand with the incorporation of machine teaching, MLOps and inferencing capabilities. The Azure AI platform now rivals Amazon SageMaker in terms of the breadth of its machine learning lifecycle capabilities for developers and data scientists.
Next Steps for Microsoft in 2019 and Beyond
The sheer number of Build announcements and future scenarios Satya Nadella covered in his keynote highlight the progress Microsoft is making across the wealth of its cloud assets and developer platforms. Few players have such a flywheel effect across their businesses that produces the level of growth and scale for developers.
As the role of the developer changes significantly over the next few years to work closer than ever before with data scientists and business leaders, as well as privacy, security and compliance departments, Microsoft’s AI will be key to help them make the transition. The release of MLOps is an example of a good step in this direction.
But Microsoft will need to improve its AI strategy. It requires more production-level features (as opposed to research) to improve AI governance, including more instruments in Azure Machine Learning for bias detection, explainability and security. It also should look to build a more comprehensive marketplace strategy for machine learning models and data sets. Moving forward, we anticipate customers will expect a trusted environment in Azure rather than GitHub to transact intellectual property in AI. The company should also prioritize the expansion of more packaged business solutions and the promotion of industry-led and business process specific partners who can help firms operationalize the technology more quickly.
Execution on each of these fronts must now be Microsoft’s priority as it presses forward in 2019.