08 Mar Which Machine Learning as a Service (MLaaS) Provider Should I Go for?

The term ‘machine learning’ may seem like rocket science for most businesses.

However, in actual fact, it certainly is, to some extent. To make it even simpler, machine learning as a service (MLaaS) is a range of services that provide ML tools as part of cloud computing services.

It helps clients enjoy the benefits from machine learning without the associated costs, time, and risk of setting up an in-house internal machine learning team. You can start building your very first working models by using machine learning cloud services, yielding valuable insights from predictions with a comparatively small team.

Now that you have learned about machine learning strategy, let’s have a look at the best machine learning platform on the market.

Microsoft Azure Machine Learning Studio

Azure Machine Learning platform has been designed for building powerful ground for both newcomers and experienced data scientists. The list of Microsoft machine learning products is similar to the ones from Amazon. However, Azure, if we talk about it today, offers more flexibility in terms of out-of-the-box algorithms.

We can divide the services from Azure can be divided into two major categories:

• Azure Machine Learning Studio
• Bot Service

Wondering What’s under the Hood of Microsoft Azure ML Studio?

In Azure Machine Learning Studio, you must complete nearly all operations using a graphical drag-and-drop interface. Now this includes preprocessing, data exploration, choosing methods, and validating modeling results. It’s important to note here that approaching machine learning with Azure involves some learning curve. But it will ultimately lead you to a deeper understanding of all major techniques in the field.

Some of the significant benefits of Microsoft Azure ML Studio are discussed below:

• The key benefit of using Azure is possibly the range of algorithms available to play with. To be more precise, Microsoft Azure ML Studio supports around 100 methods that include address classification, recommendation, regression, anomaly detection, and text analysis.
• Azure Machine Learning Services can be considered as the next-generation infrastructure in terms of building and deploying models at scale, using any framework or tool.
• The graphical interface of Azure visualizes each step within the workflow. It also supports newcomers.
• Microsoft Azure ML Studio platform has one clustering algorithm.
• Do you know what’s another big chunk of Microsoft Azure ML? It’s Cortana Intelligence Gallery – a collection of machine learning solutions offered by the community. Data scientists can explore and reuse it as per their requirements.
• When it comes to starting with ML and introducing its capabilities to new employees, the Azure product is undeniably a powerful tool.
• It enables developers to visualize data, deploy models, and work on dataset preparation – all in one place.

Okay, Now Let’s Have a Deeper Look at what the ML Services Platform Suggests:

• Python Packages: These packages include libraries and functions. These target 4 main groups of tasks: text analysis forecasting, computer vision, and hardware acceleration.
• Model Management: It’s a tool that provides an environment to manage, host, version, and monitor models that run on Azure.
• Visual Studio Tools for AI: In general, this extension is there to add tools to the Visual Studio IDE to effectively work with other AI products and deep learning.
• Experimentation: By making use of any Python tool and framework, engineers can build different models. They can further compare them, set the project to exact historic configuration, and carry on the development from any moment in history.
• Workbench: This is a convenient desktop and command-line environment product. It has evaluation tools and dashboards to track model development.

Wrapping Up

It’s easy to get lost in the variety of solutions that Microsoft Azure ML provides. If we talk about different providers of Machine Learning as a Service (MLaaS) in the market today, the comparison will bring forth various similar and diverse benefits. Not to mention that there several big players in the industry today offering these services. But Microsoft Azure ML Service, in this context, provides you with parameter settings, end-to-end lifecycle management, configuration, code sorting, keeps track of all of your experiments across your entire team, environment details, and much more to make it easy to search, rank, and duplicate any experiment your team might be engaged in.