• 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.

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The main components of Azure Machine Learning are:

• Azure Machine Learning Workbench
• Azure Machine Learning Experimentation Service
• Azure Machine Learning Model Management Service
• Microsoft Machine Learning Libraries for Apache Spark (MMLSpark Library)
• Visual Studio Code Tools for AI

Together, these applications and services help significantly accelerate your data science project development and deployment.

Read Also: What is Machine Learning?

Azure Machine Learning Compatibility to Open Source

Azure Machine Learning fully supports open source technologies. Thousands of open source Python packages, such as the following machine learning frame works:

• scikit-learn
• TensorFlow
• Microsoft Cognitive Toolkit
• Spark ML

One can execute their experiments in managed environments such as Docker containers and Spark clusters.

Azure Machine Learning is built on top of the following open source technologies:

• Jupyter Notebook
• Apache Spark
• Docker
• Kubernetes
• Python
• Conda

It also includes Microsoft’s own open source technologies, such as Microsoft Machine Learning Library for Apache Spark and Cognitive Toolkit.

In addition, you also benefit from some of the most advanced, tried-and-true machine learning technologies developed by Microsoft designed to solve on large-scale problems. They are battle-tested in many Microsoft products, such as:

• Windows
• Bing
• Xbox
• Office
• SQL Server

The following are some of the Microsoft machine learning technologies included with Azure Machine Learning:

• PROSE (PROgram Synthesis using Examples)
• microsoftml
• revoscalepy

Azure Machine Learning Model Management

Azure Machine Learning Model Management enables you to manage and deploy machine-learning workflows and models.

Model Management provides capabilities for:

• Model Versioning
• Tracking models in production
• Deploying models to production through AzureML Compute Environment with Azure Container Service and Kubernetes
• Creating Docker containers with the models and testing them locally
• Automated model retraining
• Capturing model telemetry for actionable insights.

Azure Machine Learning Model Management provides a registry of model versions. It also provides automated workflows for packaging and deploying Machine Learning containers as REST APIs. The models and their runtime dependencies are packaged in Linux-based Docker container with prediction API.

Azure Machine Learning Compute Environments help to set up and manage scalable clusters for hosting the models. The compute environment is based on Azure Container Services. Azure Container Services provides automatic exposure of Machine Learning APIs as REST API endpoints with the following features:

• Authentication
• Load balancing
• Automatic scale-out
• Encryption

Azure Machine Learning Model Management provides these capabilities through the CLI, API, and the Azure portal.

Azure Machine Learning model management uses the following information:

• Model file or a directory with the model files
• User created Python file implementing a model scoring function
• Conda dependency file listing runtime dependencies
• Runtime environment choice, and
• Schema file for API parameters

This information is used when performing the following actions:

• Registering a model
• Creating a manifest that is used when building a container
• Building a Docker container image
• Deploying a container to Azure Container Service

The following figure shows an overview of how models are registered and deployed into the cluster.

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The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation engines use machine learning to personalize online ad delivery in almost real time. Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.

How machine learning works

Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.

Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms — also called neural networks — are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation. These neural networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.

Examples of machine learning

Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly.

Machine learning is also entering an array of enterprise applications. Customer relationship management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. Business intelligence (BI) and analytics vendors use machine learning in their software to help users automatically identify potentially important data points. Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.

Machine learning also plays an important role in self-driving cars. Deep learning neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road.

Virtual assistant technology is also powered through machine learning. Smart assistants combine several deep learning models to interpret natural speech, bring in relevant context — like a user’s personal schedule or previously defined preferences — and take an action, like booking a flight or pulling up driving directions.

Types of machine learning algorithms

Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the fairly simple to the highly complex. Here are a few of the most commonly used models:

This class of machine learning algorithm involves identifying a correlation — generally between two variables — and using that correlation to make predictions about future data points.
Decision trees. These models use observations about certain actions and identify an optimal path for arriving at a desired outcome.

K-means clustering. This model groups a specified number of data points into a specific number of groupings based on like characteristics.

Neural networks. These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.

Reinforcement learning. This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.

The future of machine learning

While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence (AI) has grown in prominence. Deep learning models in particular power today’s most advanced AI applications.

Machine learning platforms are among enterprise technology’s most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today’s AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future, different tasks.

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