Azure Machine Learning is an enterprise-grade AI service that helps data scientists and developers build, deploy, and manage high-quality machine learning models efficiently. It accelerates the time to value with a strong focus on industry-leading machine learning operations (MLOps), open-source compatibility, and integrated tools. This platform promotes responsible AI practices and supports the end-to-end machine learning lifecycle.

Features:

• Accelerate Time to Value: Azure Machine Learning speeds up the process of building machine learning models by leveraging powerful AI infrastructure and orchestrating AI workflows through Prompt Flow.
• Collaboration and MLOps: The platform allows for quick machine learning model deployment, management, and sharing for seamless collaboration across workspaces, streamlining MLOps processes.
• Development with Confidence: Azure Machine Learning incorporates built-in governance, security, and compliance measures to ensure safe and reliable machine learning operations.
• Responsible AI: The tool promotes responsible AI practices, enabling the development of explainable models using data-driven decisions for transparency and accountability.

Support for the End-to-End Machine Learning Lifecycle:

• Prepare Data: This stage involves data labeling, data preparation, and dataset management for data exploration and analysis.
• Build and Train AI and ML Models: Azure Machine Learning supports building and training models, providing compatibility with open-source frameworks like PyTorch and TensorFlow. It enables deep learning and includes features like automation for tabular, text, and image data.
• Validate and Deploy: The platform facilitates model validation and deployment and offers MLOps capabilities for native model management. It ensures security, responsible AI, and monitoring for deployed models.

Azure Machine Learning for Generative AI:

• Simplify the design, evaluation, and deployment of large language model-based applications with prompt flow.
• Streamline the entire large language model lifecycle and model management with native MLOps capabilities.
• Support the use of flexible tools and frameworks, and offer world-class performance for large-scale AI operations.

Benefits:

• Accelerate time to value with rapid model development.
• Collaborate and streamline model management with MLOps.
• Build enterprise-grade solutions on a hybrid platform