Microsoft ahas several new additions to its Azure ML offering for machine learning, including better integration with Python and automated self-tuning features for faster model development.

Learn More The skill of feature engineering — crafting data features optimized for machine learning — is as old as data science itself. But it’s a skill I’ve noticed is becoming more and ...

The Azure Machine Learning Service is part of an integrated suite of models, frameworks, services, infrastructure, and deployment options. The new features of Azure Machine Learning include AutoML ...

Developed by the Microsoft Lab 1711 – the subsidiary team of the Cloud & Enterprise China Engineering ... “But by using Azure Machine Learning, we can now make recommendations to customers ordering a ...

Microsoft’s Azure Machine Learning cloud service ... data-centric approach to discovering, evaluating and engineering machine learning features. Feature discovery is often a slow, laborious ...

With features like AutoML, drag-and-drop design tools, and MLOps integration, the platform strikes a balance between ease of use and enterprise-grade sophistication. [Click on image for larger view.] ...

Microsoft’s new hybrid cloud AI capabilities also draw on Azure Machine Learning, a set of tools designed to ease neural network development. Another component of Microsoft’s AI feature set ...

management and scaling of machine learning models. Azure ML offers more features for data preparation, transformation, normalization and model training than Watson. It also comes with many built ...

The FeatureByte CEO discusses the best way to prep data for ML models, and also explains some of the most common challenges with feature engineering. Written by eWEEK content and product ...

Databricks also emphasizes security, offering features like Databricks Runtime for Machine Learning ... outside the Azure ecosystem. Databricks is tailored for data science and engineering projects.