Azure Machine Learning And Machine Learning Infrastructure Requirements

Introduction

In this article, I will explain Azure Machine learning, why to choose Azure ML, and Machine Learning Infrastructure requirements.

Azure is the most commonly used word for cloud computing platforms and services. Microsoft Azure is a growing collection of integrated cloud services. In this post, I will not explain more about Azure, rather, I will explore Azure Machine Learning.

Azure Machine Learning is a cloud-based platform for designing complete machine learning solutions where you can design, deploy, evaluate, automate, maintain, and track the models. Machine Learning Studio is a cloud-based, integrated development environment where you will have your workspace to conduct ML experiments. The platform can handle all scenarios of Machine learning, starting from Classic ML to deep learning, and is also suitable for supervised as well as an unsupervised approach. You can go for code first approach with the full flavor of writing Python or R code with the SDK. You have the option to create a workspace with notebooks (Jupiter Notebooks), taking full advantage of Python code, likewise for RStudio.

Additionally, Azure ML gives the full capabilities to design a machine learning solution with Low or No Code, using drag and drop, Designer. With the use of Designer and Azure ML, you can add a data source, do data transformation, train the model with different algorithms, as well do scoring and evaluation without writing any code with publishing options as well. Isn’t it cool!!

The service also interoperates with popular deep learning and reinforcement open-source tools such as PyTorch, TensorFlow, scikit-learn, and Ray RLlib.

Why choose Azure Machine Learning?

So far, we have discussed the traditional machine learning process, which has been used over and over to create Training Models. These training models, obviously, is the heart of ML solutions, however, having these model is only part of complete machine learning solutions. Furthermore, there are additional Machine Learning infrastructure requirements for a complete solution.

To build a complete ML solution, there are several infrastructure components requirements. You need a place to Manage the data, then again we need an environment to sufficiently build Machine Learning Model, i.e. a workspace to build ML model and run experiments.

Soon after, we need to deploy the solution to use the full capabilities of the application.

Next, we need to be able to manage access to the solution. Like giving key so that only authorized users/clients can access our Machine learning application.

Finally, there should be an environment to maintain our ml solution as data, algorithm, scopes, and sometimes requirements are changed with time.

Therefore, we have these infrastructure requirements for complete ML solutions which can be achieved through a single platform, Azure ML, that’s why it is an ideal platform for machine learning.

How are these resources being connected?

As depicted in the above picture, first, we use storage resources for ML data like blobs, tables, Azure SQL DB, etc.

Then we fetch those data to an integrated development environment for ML where we clean data, train with the algorithm, evaluate the model.

Once our Model is ready with expected accuracy, we publish or deploy using web services.

Access control is another factor to consider to expose our solution/API to clients, so that only authentic clients can use the solutions.

We have all these options available in a single place, the Azure Machine Learning platform with storage services, Azure ML studio for developing Model. Similarly, App services, Compute instances, Kubernetes services, Containerize instances, etc. Options are available for deploying the solution with authentication and authorization inbuild.

Azure Machine Learning Options

Apart from the code first and designer approaches, there are other productive options we have. For instance, Automated ML, which accelerates model development by suppressing the manual iterative process.

Another one more paradigm is added, MLOps alike DevOps.

Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows. For example, continuous integration, delivery, and deployment. MLOps applies these principles to the machine learning process, with the goal of:

Faster experimentation and development of models
Faster deployment of models into production
Quality assurance

Summary

Above all, Azure Machine Learning is the ultimate platform for Machine Learning with copybook capabilities to fulfill all requirements.

Cloud Jump, Have a good day!!!