Azure Machine Learning Workbench is a desktop application plus command-line tools, supported on both Windows and macOS. It allows you to manage machine learning solutions through the entire data science life cycle:
- Data ingestion and preparation
- Model development and experiment management
- Model deployment in various target environments
Here are the core functionalities offered by Azure Machine Learning Workbench:
- Data preparation tool that can learn data transformation logic by example.
- Data source abstraction accessible through UX and Python code.
- Python SDK for invoking visually constructed data preparation packages.
- Built-in Jupyter Notebook service and client UX.
- Experiment monitoring and management through run history views.
- Role-based access control that enables sharing and collaboration through Azure Active Directory.
- Automatic project snapshots for each run, and explicit version control enabled by native Git integration.
- Integration with popular Python IDEs.
For more information, reference the following articles:
- Data Preparation User Guide
- Using Git with Azure Machine Learning
- Using Jupyter Notebook in Azure Machine Learning
- Roaming and Sharing
- Run History Guide
- IDE Integration
Azure Machine Learning Experimentation Service
The Experimentation Service handles the execution of machine learning experiments. It also supports the Workbench by providing project management, Git integration, access control, roaming, and sharing.
Through easy configuration, you can execute your experiments across a range of compute environment options:
- Local native
- Local Docker container
- Docker container on a remote VM
- Scale out Spark cluster in Azure
The Experimentation Service constructs virtual environments to ensure that your script can be executed in isolation with reproducible results. It records run history information and presents the history to you visually. You can easily select the best model out of your experiment runs.
Real Also: What is Machine Learning?
Azure Machine Learning Model Management Service
Model Management Service allows data scientists and dev-ops teams to deploy predictive models into a wide variety of environments. Model versions and lineage are tracked from training runs to deployments. Models are stored, registered, and managed in the cloud.
Using simple CLI commands, you can containerize your model, scoring scripts and dependencies into Docker images. These images are registered in your own Docker registry hosted in Azure (Azure Container Registry). They can be reliably deployed to the following targets:
- Local machines
- On-premises servers
- The cloud
- IoT edge devices
Kubernetes running in the Azure Container Service (ACS) is used for cloud scale-out deployment. Model execution telemetry is captured in AppInsights for visual analysis.
Microsoft Machine Learning Library for Apache Spark
The MMLSpark(Microsoft Machine Learning Library for Apache Spark) is an open-source Spark package that provides deep learning and data science tools for Apache Spark. It integrates Spark Machine Learning Pipelines with the Microsoft Cognitive Toolkit and OpenCV library. It enables you to quickly create powerful, highly scalable predictive, and analytical models for large image and text datasets. Some highlights include:
- Easily ingest images from HDFS into Spark DataFrame
- Pre-process image data using transforms from OpenCV
- Featurize images using pre-trained deep neural nets using the Microsoft Cognitive Toolkit
- Use pre-trained bidirectional LSTMs from Keras for medical entity extraction
- Train DNN-based image classification models on N-Series GPU VMs on Azure
- Featurize free-form text data using convenient APIs on top of primitives in SparkML via a single transformer
- Train classification and regression models easily via implicit featurization of data
- Compute a rich set of evaluation metrics including per-instance metrics
Visual Studio Code Tools for AI
Visual Studio Code Tools for AI is an extension in Visual Studio Code to build, test, and deploy Deep Learning and AI solutions. It features many integration points with Azure Machine Learning, including:
- A run history view displaying the performance of training runs and logged metrics.
- A gallery view allowing users to browse and bootstrap new projects with the Microsoft Cognitive Toolkit, TensorFlow, and many other deep-learning frameworks.
- An explorer view for selecting compute targets for your scripts to execute.