Microsoft Azure Machine Learning Studio Reviews

A project was handed to us before we came to this new client, which involved running a machine learning experiment within ML Studio. The good thing about the solution is the entire workflow can be easily managed in ML Studio because you can track and tag datasets, different pipelines, and multiple transformations. You can add custom code to any of the transformation bits, so it's very flexible in how you design your experiments. You can either design a pipeline or run notebooks. You can do many things, and it's very flexible for many use cases.

ML Studio is very easy to maintain. It's also very portable because it has ARM templates to export to replicate your experiments in separate environments. That's useful if you move an experiment to a different resource group because you want to run a new experiment. It has a strong role-based access control that helps you keep track of who's accessing what, and it has a very good data lineage tool that allows you to version and understand each of the experiments and their results. You have a very good track of everything, and you can easily distinguish between experiments and execution times and which parts where the pipelines are failing. ML Studio gives you a lot of identifiability for each one of the components of your entire experiment.

While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy, and when your datasets need to be distributed or parallel processed. While it offers you the capability of running distributed computing, it relies on the user to configure it. It does not do it automatically as Databricks would. It is up to the user to maximize ML Studio's use. Still, suppose you do not preemptively configure it to run everything in distributed compute or parallel jobs. In that case, it will just provision a single compute cluster and take longer than other solutions that do that automatically. ML Studio relies on user configuration to run parallel or distributed jobs. When you are new and trying to experiment with it, it could make your workflows much more costly and longer than they should be.

One or two engineers can easily maintain ML Studio without much hassle.

ML Studio's pricing becomes a numbers game. When you're trying to run isolated experiments with simple datasets that are easily tracked, ML Studio does a very good job with its on-demand pricing. At the same time, provisioning the solution and some other internal tools might not be cost-optimized. It might just be directly provisioned from infrastructure direct cost. As your data scales and grows and your transformations become more complex, your cost will probably skyrocket because it will do nothing natively to help you save on that end. Other platforms help you run jobs and allow you to run them distributed with a simple configuration from the UI rather than having the optimized code to do so.

Microsoft Azure Machine Learning Studio is very robust for tracking simple experiments. But it falls short when you run when you want to build an entire machine learning framework on top of it. I rate it a seven out of ten.

Microsoft Azure Machine Learning Studio is easy to use and deploy. It has an efficient CI/CD tool.

The platform’s integration with Apache could be better.  

It is a highly stable platform. I rate its stability a nine out of ten.

It is a scalable product.

The platform’s technical support services are good.

Neutral

The initial setup is easy. I rate the process an eight out of ten. We have trained machine learning models for the installation. It requires two executives for deployment and three executives for maintenance.

The platform's price is low. I rate its pricing a four out of ten.

I rate Microsoft Azure Machine Learning Studio a seven 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. 

We're setting up the environment for our data science and IT project. It is a protected environment for protected data. So, there's a lot of architecturing in this solution.

Their support is helpful.

It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this.

I don't know about its stability yet. We're facing some issues, and we are approaching the product team for help. It might also have something to do with our environment setup. Our environment is inside V-Net, and we have a lot of security requirements.

We're working with their team to resolve the issues. Having someone to assist you makes it easier. We have someone at Microsoft to help us with it. They're very helpful.

I have worked a little bit with Open Source.

We are only testing, and we have to be very careful of the restrictions. I'm a little bit aware of the issues about ML Ops, and I am trying to see if Azure Machine Learning Studio can address those issues. For now, I would rate it a seven out of 10. I have to explore it more.

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.

in-house translation, time series and computer vision applications;  create models from scratch and just play around with data visualization.

Azure's AutoML feature is probably better than the competition.

The interface is a bit overloaded.

I've been using Azure ML Studio for about three months. 

Azure ML Studio has the same scalability as other similar solutions.

I haven't used Microsoft Azure support for this so far.

also using Amazon and Google solutions. In terms of performance, I think they're pretty much the same. Amazon SageMaker is a bit more mature.  Google Colaboratory and Vertex AI have better UI

Setting up ML Studio is very straightforward because it's a cloud thing. 

I rate Microsoft Azure Machine Learning Studio seven out of 10. I would definitely recommend it to customers. The autoML, in particular, has some very advanced features.

I am using Microsoft Azure Machine Learning Studio for my personal use.

I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model.

I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else.

I have been using Microsoft Azure Machine Learning Studio for almost two years.

Microsoft Azure Machine Learning Studio is a stable solution.

The initial setup is straightforward. I think it's a cloud service, and whenever I create a new workspace, it takes me around five to ten minutes to deploy it.

I implemented this solution.

I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs.

I would recommend this solution to new users. It's easy for people to use.

On a scale from one to ten, I would give Microsoft Azure Machine Learning Studio a nine.

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.