2024-05-14 05:30:18

Azure Machine Learning

Enterprise-grade machine learning service to build and deploy models faster


  • Company Name : Microsoft Azure



  • About Solution :

    Azure Machine Learning

     

    Enterprise-grade machine learning service to build and deploy models faster

     

    Accelerate the end-to-end machine learning lifecycle

    Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.

     

     

    Boost productivity and access ML for all skills

    Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use the no-code designer to get started, or use built-in Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning UI, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.

     

     

    Operationalize at scale with robust MLOps

    MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use ML pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate, and deploy machine learning models anywhere, from the cloud to the edge, to manage production ML workflows at scale in an enterprise-ready fashion.

     

     

    Build responsible ML solutions

    Access state-of-the-art responsible ML capabilities to understand protect and control your data, models and processes. Explain model behavior during training and inferencing and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques and use confidential computing to secure ML assets. Apply policies, use lineage and manage and control resources to meet regulatory standards.

     

     

    Innovate on an open and flexible platform

    Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, and scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter notebooks, and CLIs—or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices.

  1. Feature 1 : Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning
  2. Feature 2 : Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle
  3. Feature 3 : Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets
  1. USP 1 : Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R
  1. Price 1 : 4GiB - 36.427/month
  2. Price 2 : 8GiB - 72.854/month
  3. Price 3 : 16GiB - 146/month
  1. Feedback 1 : “With an new machine learning platforms rolling out Azure gave us a head start”
  2. Feedback 2 : “Best available tool for machine learning process”
  3. Feedback 3 : “very good software to machine learning.”
  1. Story 1 : "If I have 200 models to train—I can just do this all at once. It can be farmed out to a huge compute cluster, and it can be done in minutes. So I'm not waiting for days." Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance
  2. Story 2 : "With Azure Machine Learning, we can focus our testing on the most accurate models and avoid testing a large range of less valuable models. That saves months of time." Matthieu Boujonnier, Analytics Application Architect and Data Scientist, Schneider Electric
  3. Story 3 : "A key part of our transformation has been to embrace the cloud and the digital solutions and services that come with it. This includes a deep dive into AI and machine learning." Diana Kennedy, Vice President for IT Strategy, Architecture and Planning, BP

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