Microsoft Azure Machine Learning Studio Reviews

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Their web interface is good but the on-prem site interface is outdated.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.

We primarily use this product for its price elasticity and the product mix on offer.

The solution is very easy to use, so far as our data scientists are concerned. 

There's an excellent self-developing capability that is provided that makes the product unique.

The solution is very stable. We haven't had any issues with its performance thus far.

We've found that, if you need to, you can scale the product.

The solution is very quick to implement.

We've found that the solution runs at a high cost. It's not cheap to utilize it.

Two additional items I would like to see added in future versions are software life cycle features and more security capabilities. There should be data access security, a role level security. Right now, they don't offer this.

I've only really been using the solution for the last few months. It really hasn't been too long at this point in time.

The solution is reliable. There are no bugs or glitches. We haven't experienced crashes or freezing. It's stable. It's very good in that sense.

If a company needs to scale the solution, they should have no problem doing so. I don't see any aspect of the solution that would stop a user from expanding it as needed.

Currently, we only have a handful of users. There are only about five to seven people on the product right now.

We do plan to continue to use the product and to increase usage in the future.

We've dealt with technical support in the past. We do, from time to time, have issues, which we work with the Microsoft team to resolve.

Overall, we've been satisfied with the level of support they have provided us.

We did not previously use a different product. This is the first type of solution that we've used.

The initial setup is quick and easy. It's not complex at all. There is no installation per se. It's simply that you plug into the cloud and start using it.

For deployment, you likely need a two or three-member team. You don't need a lot of people to get it up and running. Largely they are just managers, admins or engineers, or a combination of those three.

The solution is quite expensive. It's something the organization should work on improving.

We use this product on a pay-per-use basis, Therefore, there is no licensing fee. It's embedded in the cost of using the Studio.

We're just a Microsoft customer. We don't have a business relationship with Microsoft.

Currently, it is my understanding that we are using the latest version of the solution.

I'd recommend this product to other organizations.

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

I wouldn't say it's necessarily about liking everything about the platform entirely. It's more about what do we want? In terms of machine learning, there are times that we have to get into it and customize it, etc. We can use the ready-made models that are available without really having to code encrypt them with our bitcoin code — our model doesn't need to be too complex. Deployments and everything, in general, can be automated from a CI/CD perspective as well.

I really can't see where it needs much improvement. My experience is only half-matured and is still maturing.

I don't think we have reached the stage where the customer has enough cohesion to really complain about anything. Also, a Microsoft team is personally involved which really simplifies the process.

In the machine learning world, when you are defining the model, typically people go for an interesting library of algorithms that are available. It's an imperfect scenario. The world is not as ideal as we think: how we draw a mathematical or theoretical formula is not exactly as it seems. With encryption, this uncertainty is actually much higher — that's why you need to tweak your mathematical formula or completely customize it. For this reason, my team has a development platform where they can customize code when it fails.

I have been using this solution since June.

Regarding the stability and scalability — so far so good; however, we're still exploring quite a bit. It's too early to really comment because the customer has already paid. They've just started their journey. We are yet to explore exactly what and how they want to use it. 

So far, we haven't had a situation where we have needed to raise a ticket for support on a technical front.

Currently, we're handling any issues internally because we're still in the initiation stage. It's going to take some time for us to really get our hands into it, but so far it's been a really good experience. Based on various conversations that I was part of, I think our customer really appreciates the support coming from our people.

 Compared to similar solutions, Microsoft Azure Machine Learning Studio is quite new so the initial setup wasn't much of a challenge. The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team.

I would Definitely recommend Azure Machine Learning Studio — no doubt about it, it's a full-contact solution. Having said that, it really depends on the customer's appetite and what they're comfortable with. For example, I have interacted with people who prefer a basic Google cloud platform — from an AML perspective, they just feel like it's primarily Google. Not because of AML per se, it's more from a data storage perspective, which in this case, works better.

Personally, I come from a VFA site in the financial sector. Over there, the customers are really conscious about hosting their station or their data, especially on the cloud. Typically, they are very restricted because they are not comfortable hosting customer data on the cloud. This is where I think Azure or Google or even AWS fall short — they don't play any role there. Because of this, people actually customize their solutions or model them to fit their custom sites and customer-based solutions. 

Overall, I would give this solution a rating of seven. It's a great option if you are fairly new and don't want to write too much code. As long as the model is not too complex, it's a pretty easy solution to roll out.