From self-driving cars to emotion detection, AI and machine learning enable human ingenuity, augment human experiences, and enrich human competencies. Azure Machine Learning provides the enterprise-leading platform, tools, and services to build the next AI application that will change the world.
Remove barriers to intelligent applications
Cost: As the explosion of enterprise data continues, the cost of tools, talent, and infrastructure increases. Azure Machine Learning is a cost-effective solution that leverages the cloud to give you the power and capacity when you need it. Pay only for computation you use and the models you manage and deploy.
Explosion of models: Organizations still struggle to manage and analyze their data and are now struggling to manage their models. Azure Machine Learning allows you to know which of your models to put into production and which perform best, allowing data-driven management of your code and training data, helping you manage your inventory of models intelligently.
Inaccessible AI: The short supply of data scientist has forced developers to integrate AI into their applications to meet the global demand. Azure Machine Learning lets data science and AI dev teams collaborate to boost their productivity.
Build, deploy, and manage models everywhere
Create and manage your model packages to put them into production faster in the cloud, on-premises, or edge. Guide model performance in production to ensure that the best models are in operation, then proactively retrain your models once their performance begins to degrade. All model packages can be deployed as Docker containers to put your predictions everywhere, including IoT edge. Containers enable you to score closer to the event and build realtime, high scale applications that serve your answers when you need them, no matter the demand. If you want to minimize data movement or prefer on-premises options, easily deploy in SQL Server. Democratize the consumption of your models with Excel integration or Swagger based toolchains. No matter where you want to deploy your next intelligent application, Azure Machine Learning has you covered.
Increase your rate of experimentation
Manage all your experiments whether you build them locally or in the cloud, allowing rapid desktop prototyping, then easily scale up on virtual machines or out using Spark clusters. The latest GPU technology allows Azure to engage in deep learning quickly and cost effectively. If you can’t move your data to the cloud, you can train your models onpremises with Microsoft Machine Learning Server. When agile development meets machine learning, your productivity will be unleashed. Azure Machine Learning integrates with Git to provide familiar extensible tools that plug into your workflow. Track your code, configurations, parameters, and data, so you can quickly identify the best performing model version and ensure your work can be easily reproduced.
Spend more time modeling, less time prepping
Azure Machine Learning has built-in data preparation capabilities to rapidly sample, understand, and prepare your data, both structured or unstructured. Leverage PROSE – AI built on cutting-edge work from Microsoft Research – to automatically program your data preparation and transformation steps by example, and execute for your entire data set. Once you’ve prepared your data, you can output your work in Python or PySpark and unleash it at scale.
Microsoft meets you where you are
Choose between a browser-based, visual drag-and-drop authoring environment where no coding is necessary or use a code-first approach that leverages the cloud, on-premises, and edge assets to provide power and flexibility. Azure Machine Learning is an open, flexible, and extensible platform where developers and data scientists can author models in Python, PySpark, and Scala. Leverage the most popular data science libraries and toolkits such as TensorFlow, Microsoft Cognitive Toolkit, Spark ML, scikit-learn and many more. Use your favorite IDE such as Jupyter notebooks, PyCharm, or Visual Studio. Azure Machine Learning will make it easier to develop deep learning and allow you to call its services straight from your favorite IDE. 1 Get started right away with the tools and technology you know and prefer.