Azure Machine Learning – Building and Deploying models faster with DevOps, Innovation, Responsible ML and Open Platform

Azure Machine Learning service (Azure ML) is essentially a cloud-based platform-as-a-service offering by Microsoft Azure. The Microsoft technology offers end-to-end machine learning capabilities in the cloud, from model development, model deployment to running experiments as a RESTful API endpoint. Further, the platform supports coding in both R and Python through R Studio, Jupyter notebooks, and Jupyter Lab for different user preferences.

In this blog, we explore the various aspects of Azure ML and its features and benefits to help you build and deploy your ML models much faster.

1. Bringing Effectiveness to the Machine Learning Lifecycle with Azure 

Azure Machine Learning offers a robust yet simple point-specify-click GUI to create code-free machine learning models and also provides an ideal environment for data scientists to collaborate with several tools in a single ecosystem seamlessly.

Below are some of the key features of the Azure ML service-

• Fully supports various open-source technologies such as TensorFlow, PyTorch, and sci-kit-learn
• The service introduces a new capability that allows you to simplify the model deployment process used in the machine learning lifecycle
• It can be used for any machine learning model, from classical ML to deep learning.

Azure ML empowers developers and data scientists with an enormous range of productive experiences to build, train and deploy machine learning models and encourage teamwork. Apart from this, it also helps to accelerate time to market with industry-leading MLOps (more about this in the next section) and allows you to innovate on a secure and trusted platform specially designed for responsible machine learning.

2. Azure Automated ML 

Azure Automated ML is a well-known cloud-based service that can be used to automate the process of building ML pipelines. Its goal is two folds- to identify which model to use, pre-process the input dataset, and tune hyperparameters of a given model.

Azure Machine Learning primarily offers two experiences for working with Automated ML-

• A simple user interface that is included in Azure ML Studio for limited/no-code experience customers.
• The Azure ML Python SDK, a complete Python library that allows data scientists and developers to develop ML models using all the available features of Azure ML.

The robust MLOps capabilities of Azure ML play an instrumental role in creating and deploying models at scale using fully automated machine learning workflows.

3. MLOps, The Azure DevOps to Operationalize at Scale 

MLOps or Machine Learning Operations is mainly a dedicated Machine Learning engineering culture and practice. Its objective is to unify ML system development (Dev) and ML system operation (Ops).  

MLOps is primarily based on key DevOps principles and practices. Apart from improving the quality and consistency of the machine learning solutions, it also aims to increase the efficiency of workflows.

Among the key capabilities of MLOps offered by Azure Machine Learning include-

• Develop reusable software environment and reproducible ML pipelines
• Efficiently capture all the governance data for the end-to-end ML lifecycle
• Consistently monitor all ML applications for various operational and ML-related issues
• Automate the ML lifecycle with Azure Pipelines and  Azure Machine Learning 
• Help to notify and alert on events in the ML lifecycle

Azure Machine Learning also uses certain key concepts to operationalize ML models. These include-

• Datasets and Datastores

Azure ML datasets allow you to access and interact with your data. A dataset is primarily a virtual entity that contains a reference to a data source, along with a copy of the source’s metadata. Since the data stays in place, there is no risk to data integrity and no additional storage cost.

• Workspaces

An ML workspace is a central, shareable location where multiple tasks are performed including-

• Seamless management of assets as users create and run machine learning models.
• End to end resource management for model training and deployment, including compute resources

The ML workspace also contains all other Azure resources used by machine learning models such as-

1. Azure Storage Account — It is the main data storage location for the workspace, including training datasets and Jupyter notebooks.
2. Azure Container Registry (ACR) —Its purpose is to store Docker container images being utilized during model training and deployment.
3. Azure Key Vault — It stores sensitive data required by the workspace.
4. Azure Application Insights —It enables complete monitoring and analysis of model execution and performance.
5. Deployments — It is a process where a registered model is deployed as a service endpoint.

Among the things you need here include:

• Scoring code — A script that accepts inputs, scores them using the model and then returns this model results.

• Environment — Includes all dependencies needed to run an ML model

• Inference configuration — Takes references from the environment and scoring code, along with various other components

4. ML For All Skills To Increase Productivity 

As a powerful predictive analytics service, Azure machine learning offers a seamless experience to enhance productivity for data scientists of varied skill levels systematically. This gets possible because-

• Azure ML is accompanied by the Azure Machine Learning Studio (ML Studio). This browser-based tool offers an easy-to-use, drag-and-drop interface for building powerful machine-learning models.
• It comes with a robust library of experiments and features along with best-in-class algorithms developed and tested in the real world. 
• Comes with powerful built-in support for programming languages such as R and Python, which means you can build custom scripts to customize your ML model.

Once the user has built and trained their model in the ML Studio, they can easily expose it as a Web service consumable from different programming languages.

5. Utmost Data Security with Responsible ML solutions 

Azure ML services allow you to build and deploy models much more securely with features such as network isolation and private link capabilities, custom roles and managed identity for computing resources, and role-based access control for resources and actions.

6. Azure ML-A Highly Flexible Platform that Encourages Innovation

As the main environment for dataset management, model training, and deployment, Azure ML offers a highly flexible platform that encourages innovation with the following highlights-

• Easy Integration

Azure offers powerful integration with Jupyter to write and run the code in ML Studio. Further, it also provides an ONNX Runtime to accelerate ML models across a range of operating systems, frameworks, and hardware platforms. In addition to this, the runtime can also be used to run interoperability between various ML frameworks. 

• Python Support

Python- the commonly-used programming language is fully supported in Azure ML. With Azure ML, adding custom Python code is as easy as dragging the execute python script workflow task into the model and input the code directly into the dialogue box.

• Ability to Train/Retrain Models Through APIs

Azure ML allows users and developers to restrain a deployed model with their own, fresh data programmatically through an API.  This means that as patterns in data change over time, they can easily connect to the existing model, retrain it using the new data, and begin predicting with the updates to the model.

To Conclude

Azure ML is known to make smart use of Azure services and infrastructure, to offer users complete and end-to-end management of machine learning workflows.

While there might be a learning curve involved in understanding how to handle the entire process, it is worth the effort to start training models at scale and deploy them to customers with just a single click. 

TrnDigital offers a powerful analytics and machine learning-based platform that offers essential business insights to customers across various domains. It is based on Microsoft Azure, and we use various Azure Machine Learning’s MLOps features for managing models’ end-to-end lifecycle.

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