Azure Machine Learning

Azure ML provides model mgmt, automation & monitoring for deep & supervised/unsupervised learning, plus no-code/low-code options for accurate & insightful predictions.

Published on:

April 29, 2024

We have stepped into an advanced era of machine learning, which is not hype. ML allows building data-driven applications that learn from data and improve over time. The following statistics from Forbes highlight the contribution of machine learning to the modern business world.

  • The worldwide machine learning market is expected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%.
  • $3.1B has been raised for machine learning companies with investments of more than 4400 companies.
  • North America (80%) leads in ML adoption, followed by Asia (37%) and Europe (29%).
  • 74% of data scientists and C-level executives use ML for performance analysis and reporting.

Despite the buzz surrounding ML, a Gartner report says that 85% of AI and Machine Learning Projects fail to deliver, and only 53% of projects make it to production. Successful execution of ML projects requires months of engineering investments. Fortunately, Azure Machine Learning helps expedite the journey of ML adoption for enterprises. This blog will cover seven key Azure Machine Learning benefits, showing you how  Azure ML can be lucrative for your enterprise.  

Azure machine learning services offer a cloud-based environment to train, deploy, manage, automate and monitor ML models across an enterprise. Azure ML supports deep learning, supervised, and unsupervised learning to deliver accurate and insightful predictions. Furthermore, Azure ML also supports no-code or low-code options to popularize machine learning among the non-tech community.

Let’s dive deep and see the advantages of using Azure machine learning.

1. Offering Machine Learning as a Service

Machine learning as a service (MLaaS) covers any service that offers ML solutions as part of its cloud computing solutions. Small and mid-sized businesses (SMBs) prefer MLaaS due to its reliability, cost-effectiveness, and ability to handle infrastructural issues such as data preparation, model training, and evaluation.

Azure ML comes with Microsoft Azure public cloud platform and is recognized as a prominent MLaaS provider. But it distinguishes itself from other MLaaS providers with its unique features.

  • Comprehensive ML support with hundreds of ML algorithms
  • ML for all skill levels
  • Azure Machine Learning APIs
  • Data pre-processing is made easy with sample modules

The beauty of Azure ML comes with its pay-as-you-go service. Further, Azure ML allows you to build applications without having to set up complex hardware or software. You need to subscribe and use what you need, and Azure takes care of the deployment and maintenance of your ML applications.

2. Machine learning simplified with Azure ML Studio

Azure ML Studio is an integrated ML development product that ensures a flawless ML development experience for data models, pre-processing modules, ML algorithms, and other elements. With Azure Machine Learning Studio, ML development becomes simplified and user-friendly. Azure ML studio comes with drag-and-drop features and an interactive design interface, making the execution of training experiments, monitoring results, and linking most easy.

Azure ML Studio facilitates the easy implementation of predictive models and algorithms as a web service. Easy web services deployment of ML models enables other cloud users and applications to use ML applications directly without manual help.

Working with Azure ML studio is a pleasing experience with its organized mind map format and visual connections between datasets and modules. Azure ML studio has a workspace environment that provides numerous sample experiments as a great starting point.

3. Comprehensive algorithm support

Azure ML offers a comprehensive range of machine learning algorithms, including linear regression, decision trees, clustering, and logistic regression. Implementing ML algorithms has become very easy with the built-in drag-and-drop features. You must fundamentally understand algorithms and data-wrangling expertise to deliver the best models using Azure ML.  

Azure ML empowers professionals with superior visualizations, thereby reducing coding requirements and streamlining the properties of algorithms. Azure ML enables easy importing and fine-tuning training data and helps save on cost and effort. Azure’s comprehensive collection of predictive algorithms and their drag-and-drop-based implementation is a great value proposition.

4. Competitive pricing plan

Microsoft provides a classic Azure Machine Learning version and Azure ML Studio just by signing up for a Microsoft account. This basic version offers free access to Azure ML without needing an Azure subscription or expiration date. Other striking features include the following—

  • Max 10 GB storage space
  • Max 100 modules per experiment
  • Support for predictive web services, Python, and R

The enterprise-grade standard version comes with the following benefits—

  • Azure subscription and SLA
  • Unlimited storage space--BYO
  • Additional support and services include better storage and modules for advanced ML experiments.

5. Seamless and faster web service deployment

Another key benefit of Azure ML is that it allows quick and easy implementation of web services. Once your ML training experiment is ready, you can deploy predictive models as a web service. Drag and drop enabled data sets, algorithms, and linking ensure seamless web services implementation for predictive models.

You can access and use any web service through any connected device with authorized credentials. Azure ML web services are a class apart and preferred because:

  • Azure ML manages the process for you and reduces developer efforts
  • Azure ML ensures faster deployment of experiments

6. Azure and Cross-platform Integration

Interoperability defines the future of AI and its subsets. Azure ML services offered by the cloud allow you to access and test your models worldwide, through any connected device, and without compromising your data security.

With a secure internet connection, you can access ML services and manage your models with Azure ML’s support for diverse programming languages, operating systems, and databases.

With Azure Machine Learning solutions, enterprises can retrieve and access their data anywhere and anytime. It enables direct data access through numerous sources like Azure SQL, Hive Query, on-premises data sources, and many others.

7. Comprehensive Documentation and Community Support

Lastly, Microsoft Azure provides comprehensive documentation with tutorials, examples, and references to help enterprises deploy, manage, and access ML solutions swiftly. Also, Azure Community Support allows users to interact with experts and get answers to specific queries.


These seven essential Azure Machine Learning benefits can propel business growth. Deploying ML solutions and managing big data has always been a complex challenge for enterprises. But with Azure ML, building advanced ML solutions has become more simplified, provided you rely on the right AI expert to get the most out of Azure ML offerings.

Attri helps you to shape your AI adoptions with relevant ML knowledge and Azure ML expertise and to leverage Microsoft Cloud Data Platform Solutions. As a Microsoft Azure ML consultant, we help you plan a concrete AI roadmap backed by Azure capabilities and extract the most out of Azure Machine Learning.

Attri helps enterprises in their AI adoption journey, from use case identification to PoC development to AI execution. No matter how much you have progressed with your enterprise AI adoption, Attri’s experienced resources make your AI implementation journey rewarding.

For more information on Azure Machine Learning Services from Attri, Get in Touch with us Now.