Explore applied machine learning techniques, focusing on deep learning applications in real-world industry scenarios.

Learn about applied machine learning techniques through real industry case studies using TensorFlow, focusing on practical outcomes.

Solve real-world industry problems using machine learning and TensorFlow through hands-on case studies.

By optimizing your development for performance, you can ensure that your work is useful and appreciated by users. As a finance and operations apps developer, you should understand how your designs can be properly optimized for performance.

This module teaches education partners to configure Microsoft Purview Information Barriers (IB) for unique education needs. Part of the Partner Success Series.

Learn Python for Natural Language Processing, the field behind chatbots, search engines, and autocorrect.

Learn Python for Natural Language Processing, the field behind chatbots, search engines, and autocorrect.

Learn Python for Natural Language Processing, the field behind chatbots, search engines, and autocorrect.

Define networking configurations for Kubernetes environments and their pros and cons, then choose the best option for your scenario.

Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.

This course describes how to articulate the value of software engineering principles such as Test Driven Development, Test Automation, Chaos Engineering, and Key Reliability Architecture Patterns.

Define storage solutions for applications, their pros, and cons, then choose the best option for your scenario.