Microsoft Azure Machine Learning Studio Reviews

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.

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.

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.

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.

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.