Azure Machine Learning Workbench: Workbench is an end-user Windows/MacOS application that handles primary tasks for a machine learning project, including data import and preparation, model development, experiment management and model deployment in multiple environments. Azure Machine Learning Experimentation Service: This service interoperates with Workbench to provide project management, access control and version control (through Git). It helps support the execution of machine learning experiments to build and train models. Experimentation also focuses on the construction of virtualized environments, which enables developers to properly isolate and operate models, and records details of each run to aid in model development. Azure Machine Learning Model Management: This service helps developers track and manage model versions; register and store models; process models and dependencies into Docker image files; register those images in their own Docker registry in Azure; and deploy those container images to a wide assortment of computing environments, including IoT edge devices.
Earlier today, we disclosed a set of major updates to Azure Machine Learning designed for data scientists to build, deploy, manage, and monitor models at any scale. This has been in private preview for the last 6 months, with over 100 companies, and we're incredibly excited to share these updates with you today. This post covers the learnings we've had with Azure Machine Learning so far, the trends we're seeing from our customers today, the key design points we've considered in building these new features, and dive into the new capabilities. We launched Azure Machine Learning Studio three years ago, designed to enable established data scientists and those new to the space to easily compose and deploy ML models. Before the term was in use, we enabled serverless training of experiments built by graphically composing from a rich set of modules, and then deploying these as a web service with the push of a button. The service serves billions of scoring requests on top of hundreds of thousands of models built by data scientists.
Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind the digital transformation of business. Our mission is to bring AI to every developer and every organization on the planet, and help businesses augment human ingenuity in unique and differentiated ways. Developers and data scientists are at the heart of driving this innovation force and we are committed to providing them the best tools to make them successful.
Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind the digital transformation of business. Today, at Microsoft Ignite 2017, as we engage in conversations about digital transformation with over 25,000 customers and partners, I am pleased to share some of our latest and most exciting innovations in AI development platforms. These announcements – which span Azure Machine Learning (AML), new Visual Studio tools for AI, Cognitive Services and new enterprise AI solutions – demonstrate our mission to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in unique and differentiated ways. Today we are announcing a set of powerful new capabilities in AML for developers to exploit big data, GPUs, data wrangling and container based model deployment. Let me tell you more about these below and for a deep dive please review this AML blog.
Microsoft, just like many of its competitors, has gone all in on machine learning. That emphasis is on full display at the company's Ignite conference this where, where the company today announced a number of new tools for developers who want to build new A.I. models and users who simply want to make use of these pre-existing models -- either from their own teams or from Microsoft. For developers, the company launched three major new tools today: the Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench and the Azure Machine Learning Model Management service. In addition, Microsoft also launched a new set of tools for developers who want to use its Visual Studio Code IDE for building models with CNTK, TensorFlow, Theano, Keras and Caffe2. And for non-developers, Microsoft is also bringing Azure-based machine learning models to Excel users, who will now be able to call up the AI functions that their company's data scientists have created right from their spreadsheets.