Azure Machine Learning

Read the latest announcements about Azure Maps, our machine-learning tools, how we give clinicians the right data, and more.

Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features?

The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.

During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service.

Artificial intelligence (AI) has become the hottest topic in tech. Executives, business managers, analysts, engineers, developers, and data scientists all want to leverage the power of AI to gain better insights to their work and better predictions for accomplishing their goals.

At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle.

With the exponential rise of data, we are undergoing a technology transformation, as organizations realize the need for insights driven decisions. Artificial intelligence (AI) and machine learning (ML) technologies can help harness this data to drive real business outcomes across industries.

AI is fueling the next wave of transformative innovations that will change the world. With Azure AI, our goal is to empower organizations to apply AI across the spectrum of their business to engage customers, empower employees, optimize operations and transform products.

Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system.

We are truly at a unique tipping point in the history of technology. The pace of growth is more rapid than ever before, with estimates of more than 150B connected devices and data growth up to 175 Zettabytes by 2025.

DevOps is the union of people, processes, and products to enable the continuous delivery of value to end users. DevOps for machine learning is about bringing the lifecycle management of DevOps to Machine Learning.

When it comes to executing a machine learning project in an organization, data scientists, project managers, and business leads need to work together to deploy the best models to meet specific business objectives.