Microsoft Azure Machine Learning Studio Reviews

Our organization employs people with diverse professional backgrounds. We have sociology, mathematics, and statistics backgrounds. We employ these people within our data science team. They require a certain amount of programming skills.

The good thing about Azure Machine Learning is they have a drag and drop feature. You can use Azure Machine Learning designer for all of your data science teams.

Any non-programmer can adopt it. All he needs is statistics and data analysis skills.                                                                                             

I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer.

I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.

We have been using Azure for three months. We have been exploring it for different use cases. 

I haven't used it long enough to have found any bugs in our current system. If there were bugs I would definitely report it on their website.

We didn't have any problems with the setup. It was pretty straightforward.

It's an easy tool. They have a good level of resources and we are pretty low with resources as far as data science is concerned.

Azure Machine Learning offers an opportunity for those who haven't been introduced to Azure programming. You can use the data analytics and their statistics skills to build and deploy data science solutions that can be beneficial for society and for different organizations.

I would rate it a seven out of ten.

We primarily use the solution for data science.

The technical support of the solution is great. We have a contract with Microsoft and they are very good. 

The solution is very fast and simple for a data science solution. 

The pricing is very good.

The solution should be more customizable. There should be more algorithms. 

The solution needs more functionality.

We're at the beginning of the process and have only been using the solution for a few months.

The solution is very stable. We haven't had issues with bugs or glitches. We haven't experienced any crashes.

The solution is extremely scalable. This is because it's on the cloud. If a company needs to scale up they can do so quickly and easily.

At the moment, we have five employees using the solution. They are data scientists and engineers.

The solution offers very good technical support. Microsoft is well represented here in France. We've been very satisfied with support so far.

Previous to this solution, we had an improvised product. It wasn't a native cloud solution. We ended up choosing Azure Machine Learning because Azure is our management product. It made it easy for us to switch to the cloud.

The initial setup was very easy because it's a cloud solution. With the cloud option, you just subscribe, and you are ready to go in a few minutes.

I would recommend the product. It's a solution that can cover all the processes from data preparation to mobilization data while serving the clients and production. 

I'd rate the solution eight out of ten.

We're using the solution in order to give the customer a 360 degree view. Also, we use it if clients want to do machine learning with AI at a more reasonable cost.

Right now, we are just testing the customer insights from Microsoft.

The UI is very user-friendly and that AI is easy to use.

Usually, we also use the machine learning studio to build up the data logistics in machine learning.

On the customer side, the solution should do more to push companion marketing.

When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.

The solution should simplify switching between platforms in the studio.

I've been dealing with the solution for two years.

I've only used the solution a couple of times. I haven't noticed any bugs and when I used it, it worked quite smoothly.

I don't have enough knowledge about the solution's scalability to be able to comment on it. Right now, we have about 5,000-6,000 users on the solution. Most are data scientists, and IT admins.

I've personally been in touch with technical support and I found them quite helpful.

I've only ever worked with Microsoft Azure. We didn't previously use a different solution.

The initial setup is very straightforward.

Our clients do the implementation with the help fo consultants like us.

The pricing and licensing are difficult to explain to clients. Their rationale for what things cost and why are not easy to explain.

I'm a consultant. Our company is partners with Microsoft.

Users will find it easy to get into Azure. Even if they aren't always in touch with Azure, they'll find themselves in touch with the dynamic field. Users have to get into Azure because once they get into the cloud, they should have some basic understanding of Azure itself.

I'd rate the solution eight out of ten. However, I don't know their competitors, so I can't really compare them to others on the market.

Azure Machine Learning Studio works with our ERP solution.

The most valuable feature is data normalization.

The data cleaning functionality is something that could be better and needs to be improved.

There should be special pricing for developers so that they can learn this solution without paying full price.

I have been using Azure Machine Learning Studio for more than two years.

This is a stable solution.

I believe that it is scalable. At this time, we have not more than ten users. These include programmers, as well.

I have been in contact with technical support and they are good. I am happy with their response time.

The initial setup is straightforward and not too complex.

We did the implementation by ourselves.

From a developer's perspective, I find the price of this solution high. If somebody wants to learn how to use this platform then they have to spend money doing it. I know people who are interested in learning it but do not want to pay the full cost.

Microsoft Azure Machine Learning Studio is a good solution that would recommend to others, but I would like to see more support and more information available for developers.

I would rate this solution an eight out of ten.

Exploration of connections between biodata and psychometric test results.

Visualisation, and the possibility of sharing functions.

Operability with R could be improved.

The first time that I used this tool was in a project related to bike usage in the city of Boston. This project was part of a course that I concluded some months ago. In this project I used components to read data, for exploratory analysis, for steps of data munging, to split data, select hyperparameters, and some machine learning algorithms. In some steps I needed to insert R modules to apply some data transformation.

The target of this exercise was to predict bike usage in a day.

With this tool we could have all benefits of a cloud environment, such as scalability and access to machine-learning applications. These features are very important when you have large datasets and critical applications.

• It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
• When you import the dataset you can see the data distribution easily with graphics and statistical measures.
• Easy to deploy and provide the project like a service.

For my project/exercise, this tools was perfect. I would like to see modules to handle Deep Learning frameworks.

No issues with stability.

No issues with scalability.

I didn’t need to use the support, but this tool has great documentation.

Nowadays I use Python (Anaconda and Jupyter Notebook) and R (RStudio) to create my solutions and machine-learning models.

It was very simple and straightforward. It is really simple to start building a project.

There are two kinds of licenses, Free and Standard.

Free

• 100 modules per experiment.
• 1 hour per experiment.
• 10GB storage space.
• Single Node Execution/Performance.

Standard – $9.99/seat/month (probably a data scientist)

• $1 per Studio Experimentation Hour. You will pay according to the number of hours your experiments run.
• Unlimited modules per experiment.
• Up to seven days per experiment, 24 hours per module.
• Unlimited BYO storage space.
• On-premises SQL data processing.
• Multiple Nodes Execution/Performance.
• Production Web API.
• SLA.

You will be able to create your machine-learning project and extract insights from it just by dragging and dropping components and adjusting some parameters. This tool is very user-friendly, so without a lot of programming skills you can build machine-learning projects. 

If you need more control over machine-learning modules you will need to add R or Python modules to create a customized machine-learning model.

My  primary use of ML Studio is to experiment with different algorithms and learn the techniques of machine learning. In the meantime, I have developed a few models related to finance. One of the predictive models I designed was an Invoice Discrepancy Prediction model using a Multiclass Neural Network algorithm. This model predicts if an invoice will have a variance of some sort when checked against the purchase order, before the payments are to be processed.

Thanks to the model I designed, the productivity of processing invoices has increased by over 11%, because the team members only verify invoices that are discrepancy-free now.

• Split dataset
• variety of algorithms
• visualizing the data
• drag and drop capability 

are the features I appreciate most. 

The capability to model the data by finding empty cells and filling missing values by deriving the median and more, are great features that makes the job way easier.

I personally would prefer if data could be tunneled to my model through a SAP ERP system. It also needs features of Excel, such as Pivot Tables, integrated.

To create quick data analytic experiments, without incurring the time and cost of spinning up servers, setting up Hadoop, etc. 

Although MLS makes it very easy to deploy the resulting machine-learning models via REST API, I primarily use MLS as a means to quickly spin up experiments and create proof of concept models.

Not widely adopted at my old workplace, I only used this to create quick proofs of concept to try to convince management of the viability of a project.

MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.

The easy drag and drop can create simple data science experiments. Low barrier to entry allows large number of candidates get started.

The graphical nature of the output makes it very easy to create PowerPoint reports as well.

Enable creating ensemble models easier, adding more machine learning algorithms.

Out of about 150-plus MLS experiments I have done, maybe two or three bugged out. Interestingly enough, those are the ones I can’t delete out of the account.

Scalability, in terms of running experiments concurrently: Good. At max, I was able to run three different experiments concurrently.

Scalability in terms of deploying models: Unknown, I never deployed on Azure.  But I would guess REST API could probably easily handle a few K worth of hits per second, since that is how Microsoft is going to get paid.

Never used it.

The only other solution beyond this would be standard tools used by data scientists, like R, Python, etc. All of these would have a fairly high barrier to entry, requiring programming experience. The main selling point of MLS is the low barrier to entry, where even tech-savvy business people can use it.

Simple. Create MLS live account (preferably paid ones), open MLS, done.

Caveat: Different organizations have different attitudes towards cloud use, especially with sensitive data. At Bridgestone, the hardest part was getting corporate approval to allow me to upload heavily treated, sensitive data to a cloud platform.

To use MLS is fairly cheap. Even the paid account is something like $20/month,  unless you are provisioning large numbers of VMs for a Hadoop cluster.

The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API.

If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

R and Python.

Python + Pandas + scikit-learn: 

Pros: 

• scikit-learn offers better performance for extremely large data sets
• Large-data manipulation tools
• Fairly good set of ML algorithms

Cons:

• High barrier to entry, in terms of skill and knowledge
• Fairly labor intensive to create large number of experiments

R + caret:

Pros:

• Very good amount of ML algorithms (so many it may cause paralysis from too much choice, 200-plus algorithms)
• Good performance, unless the data set is extremely large

Cons:

• High barrier to entry
• Data manipulation is a pain, you probably want to use another tool to pre-treat the data before loading it into R dataframes

For data science professionals or programmers I would rate this solution a four out of 10. A major feature is missing: creating ensemble models. This can be achieved with the tool, but it's clumsy and slow.

For marketing or business professionals I would rate it an eight out of 10. It has a low barrier to entry, and can quickly create models that can be used for proof of concept and justify further investment in a full data science or Big Data project.

R and Python, in my mind, are still the way to go for a true data science/predictive analysis project. MLS's value is the ease of use and low barrier to entry. If one is not a programmer or statistician, MLS is a good way to get a project started, create a proof of concept.

I have used it to deploy predictive models in the healthcare sector.

It has helped in reducing the time involved for coding using R and/or Python. Also, web service is quite easy and convenient to use for clients. 

Its ability to publish a predictive model as a web based solution and integrate R and Python codes are amazing. It helps in building customized models, which are easy for clients to use.

It could use to add some more features in data transformation, time series and the text analytics section. Microsoft should also include more examples and tutorials for using this product.

• Feature-based selection
• Compute
• Data services.

Simplified development as scripts can be designed and implemented in real time.

I would like to see better prediction and analysis.

We have used it for a few months.

Good support is available when needed.

Stable at moment.

There are no scalability issues at the moment as data volume is still low.

Customer service is good.

Technical support is good.

We did not use a solution previous to this one.

It was complex to setup the workspace, but once it was done, we were good to go.

We did the implementation in-house.

The ROI was 36%.

The setup is a little complex, but it is worth it when it comes to security and efficiency.

We thought of doing this traditionally from scratch, but the Azure work space gives you the opportunity to utilize the environment and provide service in the shortest time possible.

For the best, reliable results, it is the best solution to have in mind. Try it out.