The global pandemic situation has changed the world's view on technological interventions and innovations. Carrying their corporate mission of “Empowering every person and every organization on the planet to achieve more”, the big tech giant Microsoft hold their annual developer conference “Build 2020” as a 48-hour virtual event.
Being virtual didn’t get down the excitement the conference creates among the developer community as well as within the enterprises. Here are few exciting announcements Microsoft did on Build 2020 related to AI, the hottest topic in the table right now.
Since AI is becoming a critical part across all industrial domains, safety, privacy and responsible use of AI related intelligent applications is a key area to focus on. Responsible ML allows the developers to control and protect the machine learning models develop through Azure Machine Learning while ensuring the human interpretability of the predictive models.
Here are the main open source libraries Azure AI has introduced under Responsible ML
Bot Framework facilitate for developing intelligent chatbot agents. Microsoft is making adaptive dialog capability generally available by enabling bots to switch contexts within a conversation. These are some main features came out recently related to bot development.
Azure cognitive services democratize AI, by reaching out every developer to leverage the power of complex machine learning applications just through a few clicks. Several new features on Cognitive services have been announced with Build 2020.
Other than these updates, speech to text in Cognitive service is coming up with 30% improvement in accuracy and 15 new voices in speech.
Azure Personalizer Apprentice mode feature – Personalizer delivers personalized and relevant experience for your application user through an AI powered intelligent backend. The new apprentice mode allows the Personalizer API to learn in real time alongside existing solutions without being exposed to users until it delivers performance results according to desired KPI goals.
Azure Cognitive Search is a fully managed search-as-a-service offered by Microsoft. It provides developers APIs and tools for adding a rich search experience over private, mixed content in web, mobile, and enterprise applications. With Build 2020 Azure search comes with Azure machine learning integration and custom search ranking as preview features enabling the developers to add the same natural language stack capabilities Bing and Microsoft Office are equipped with for their application development.
ONNX runtime is an open source machine learning model runtime which helps to train and inference machine learning models on any platform, hardware or underlying framework. Microsoft have announced some key optimization techniques for Turing model, which is the largest model in the world. The open source library is now available on GitHub to be optimized by ML engineers.
Project Bonsai is a machine teaching service for building and operating autonomous systems. This is going to be the next revolution of Industry 4.0 era, where the AI powered process automations going to take place with Azure Machine Learning. Project Bonsai allow engineers to apply their subject matter expertise to accelerate the development of intelligent control systems without the need for data science skills.
Treating your infrastructure as code is becoming more and more necessary these days. Writing these instructions becoming challenging too. In Azure we use ARM templates to define the resources and associate them with a deployment pipeline. But ARM templates are quite complicated and they are not everybody’s cup of tea.
Azure MachineLearning Service provides four main compute options each with a specific purpose attached to it. In this post we will go through each of those and see where we can occupy them in your ML experiments.
In the era of Industry 4.0 where data and predictive analytics players the major role, developing machine learning pipelines have become an essential in intelligent application development.