Nearly seven years after its debut as a preview, the Visual Studio Code extension for Azure Machine Learning has hit general availability. "You can use your favorite VS Code setup, either desktop or ...

Developers can now build, test, and deploy applications powered by OpenAI’s gpt-oss models within the AI development platform ...

Evaluating algorithms' efficacy often takes a lot more effort, as Johns Hopkins Machine Learning and Healthcare Lab Director Suchi Saria explained, with tips, at the HIMSS Machine Learning and AI for ...

The strategic fusion of cloud and AI is helping to reshape what enterprises can achieve. Cloud platforms no longer simply provide cost savings—they're able to deliver the elasticity, scale and ...

Building large-scale AI/ML systems AI/ML systems intersect with machine learning theory and software engineering.

Overview: Building AI models begins with clear goals, clean data, and selecting appropriate algorithms.Beginners can use tools like Python, scikit-learn, and Te ...

Microsoft’s cloud-based AI development environment, now in public preview, takes a more streamlined approach to building AI-powered applications.

Offers advanced AI-driven predictive modeling, data preparation, and automation tools for enterprises seeking scalable ...

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

With KubeCon Europe taking place this week, Microsoft has delivered a flurry of Azure Kubernetes announcements. In addition to a new framework for running machine learning workloads, new workload ...