Azure Machine Learning Reviews and Ratings

Use Cases and Deployment Scope

In the AI era, we need to build and deploy the machine learning model. Currently in our project is using the Azure Machine learning studio to preprocessing, cleaning, training and deployment of ML model as client requirement. As my knowledge in my team are using the Azure ML Studio. Currently, we are working to build the semantic text analysis of the documents.

Likelihood to Recommend

<div>For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.</div><div>All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.</div><div>If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.

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Use Cases and Deployment Scope

I create data science learning materials on Azure that require no coding. I use publicly available property data from Hong Kong island and surrounding areas. I teach my students how to preprocess the data, clean it up and create a hypothesis based on the type of data. We apply learning algorithms on the data and improve on the mode. The dataset was relatively small yet it took a while for the platform to get the analysis.

Likelihood to Recommend

Azure can be a more unified product. It feels like 10 different tech teams were building it but we're not talking to each other. An example is when the user needs to know what is the next step. Automatically saving a previous state is very helpful as new users are usually not aware of the functionality.

Use Cases and Deployment Scope

Currently, it is used for our information technology sector to implement machine learning features in-house. The idea is to explore models and perform some experimentation. It's used to find Machine Learning solutions for internal use in the company. The Microsoft resources in this tool make it easier to use machine learning, like the use of visual interfaces and how they manage deployment.

Likelihood to Recommend

<div>It is good to quickly and easily deploy a model for Machine Learning. It has a few coding aspects that enable machine learning that at first sight can be a problem for non-machine learning specialists. The system tries to gets the easiest results as possible.</div><div>

It is less appropriate for complex systems and for detailed results to be analyzed.

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Use Cases and Deployment Scope

I was the president of an MBA class that used Azure ML to run analytics models. The tool was used by 40 students. We analyzed a few datasets to understand the tools, and afterward, we were able to create a few analytics products based on Azure ML.

Likelihood to Recommend

Well suited:

- Run a machine learning model the fastest and easiest way;

- Working with an organization with no coding background;

- Trying to get the most of data the cheapest and easiest way possible;

- Introducing analytics and machine learning concepts to an organization or class;

Less appropriate:

- Running complex Machine Learning models;

- Visualizing data more deeply;

- Running new analytics models;

- Running heavy statistical models;