Microsoft Azure Machine Learning Studio Reviews

This solution can be used for data pre-processing, interactive data analysis, automated training, and pre-processing pipelines.

The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps.

In the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces. This would be something that would be helpful. Additionally, a better version for traceability functionality regarding data would be beneficial.

I have been using this solution for approximately six months.

The solution is stable.

I have found Microsoft Azure Machine Learning Studio scalable.

We have approximately eight people using the solution in my organization.

I have previously used Databricks. We switched to this solution because it provides better automation capabilities, easier to use external code, and allows the use of other tools, such as Docker containers.

The installation is easy. However, there is a bit more to do than with the installation of Databricks. The time it takes for the installation is approximately one day with a two-person team.

We use one engineer for the implementation and maintenance of the solution.

There is a license required for this solution.

I would recommend this solution to others.

I rate Microsoft Azure Machine Learning Studio a seven out of ten.

My primary use is for machine learning applications.

The AutoML is helpful when you're starting to explore the problem that you're trying to solve. It helps automate some of the applications of the algorithm.

The AutoML feature is very basic and they should improve it by using a more robust algorithm. It lacks deep learning type algorithms but works great for the basic classification and regression models.

I have been using the Azure Machine Learning Studio on and off, or a few months. I have not used it consistently for a significant period of time.

From my experience over the past few months, I've found it to be pretty stable. I don't know how stable it would be if operationalized.

From my experience, I think that it's scalable.

Technical support is pretty good at answering questions, and the documentation is pretty clear to understand.

Compared to their big competitor, it's much easier to set up.

I work with a data architect who does the setup. I have not personally had to do it.

We are in the process of deciding which machine learning solution we want to use. I have been dabbling with Azure and we're deciding whether to implement it versus another cloud platform.

I haven't done any research into what features they have on their roadmap.

Overall, I think that this is a comparable product.

I would rate this solution a seven out of ten.

We primarily use this solution for data analytics and model building.

The interface is very intuitive.

It is very well organized and the components can be utilized through drag-and-drop.

The data preparation capabilities need to be improved. Using this product, I can not prepare the data very much and this is a bottleneck in machine learning.

There are some features that are not supported, so I have to use either Python or R to accomplish these tasks.

I have been working with the Azure Machine Learning Studio for between six and seven years.

Up to this point, we have not faced much in terms of issues with stability.

Scalability-wise, we have not had to deal with any limitations. The only problem is that when certain options are not there, we have to use Python or R to handle those tasks.

We have not faced any problems so I have not spoken with technical support.

The initial setup is very straightforward. It is not difficult to do.

I feel that this is a great solution. Even for people from the business side, this is a very good product. It is so intuitive that all of the information is there. The interface takes care of the most complex part, which has to do with the modeling. 

I would rate this solution a nine out of ten.

We initially moved to this solution because our company needed to complete a system upgrade. We had to move the Db2 data to a AS400 system. 

Their web interface is good but the on-prem site interface is outdated. This solution could be improved if they could integrate the data pipeline scheduling part for their interface. When we are scheduling, they provide only one exclusion per day in the initial scheduling. We then have to configure it through the Linux front jobs if we want a high value job. It would help us and our customers if this was possible from the initial interface itself.

I have been using this solution for a few months. 

This is a stable solution. 

We have had limited engagement with the customer support team but when we have needed their help, they were helpful. 

Positive

The infrastructure and the software configuration part was done by one of my teammates. It was completed in two working days. We did experience some issues with the board communications which extended the time to complete the setup. This was only for the DataStage installation which is one of many components of this solution.

I would advise others to identify the communication between servers and the client tools correctly as well as the user allocation for those. If you are working from a client environment and connecting to the server, it is important that the configuration is done correctly.

I would rate this solution an eight out of ten. 

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.

My primary use case is for supervised and unsupervised learning models.

The most valuable feature is its compatibility with Tensorflow.

In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data.

I've been using this solution for a year.

The stability is questionable, given that Microsoft will be retiring the classic version of this product in 2024, and it's unclear how this will affect projects created on the classic version.

This solution is scalable.

The initial setup was straightforward, though you do need some experience with Azure administration in order to install it.

I evaluated Amazon SageMaker, which is a bit more advanced than Azure Machine Learning, with more functionalities and only a slightly higher price.

I would rate this solution as seven out of ten.

I use the solution for learning purposes for the most part.

Personally, I got interested in data science and machine learning due to using this product. After some time with it, these topics didn't intimidate me.

The solution is very easy to use. It's user-friendly and simple to navigate.

The initial setup is very simple and straightforward.

The solution is quite stable.

It's the first software that I've used in terms of machine learning. Therefore, I don't have anything to compare it to, however, it was okay for me. I didn't have any problems or anything.

Maybe it can be integrated with something else. For example, business analytics. That way, you could also give creative reports. It's possible it could be integrated with the Power BI, as it's also Microsoft. That said, I'm not really sure. It if isn't possible, it's something they could consider for a future release.

Microsoft needs to be sure to monitor the security and ensure they are constantly updating it.

It would be nice if the product offered more accessibility in general.

I've only been using the solution for a short amount of time. It's just for a course at school.

The solution is stable. I didn't have any lags or anything. It was smooth. There are no bugs or glitches. I don't recall it crashing or freezing on me.

The solution seems to be able to work well for companies both large and small. However, I did not personally attempt to scale it.

I never really dealt with technical support directly. I have my teacher to teach or to ask questions to. He would often recommend these online tutorials to learn about the solution as well. I never really thought of asking a chat box, for example, of Microsoft, where I could type any help. I never really considered it. Therefore, I can't speak to how helpful or responsive they typically are.

I did not use a different solution. This is the first solution I used for machine learning.

The initial setup was not difficult or overly complex. It's very straightforward, very simple, and very easy to understand. 

Everything is just written down in a way that was an easy way to understand, even for someone who isn't used to the packages of Microsoft.

I handled the deployment myself. I did not need the help of a consultant or integrator.

I'm just a student. I was learning about machine learning via this product.

I'm not sure which deployment model we are using.

I would rate the solution at an eight out of ten.

I would advise other potential users to just start using it. If they really want to learn it, it will take a bit of time. Even though it's easy to use, you need some knowledge in data science. That will help make the process easier.

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.