Microsoft Azure Machine Learning Studio Reviews

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.

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.

I use Azure Machine Learning Studio in my project to find solutions and build prototypes. It is mainly for fund management purposes and creating tools for specific cases.

The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices.

Microsoft Azure Machine Learning Studio could improve in providing more efficient and cost-effective access to its tools for companies like mine.

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

I would rate the stability of Azure Machine Learning Studio at around seven out of ten. Occasionally, there are minor hiccups, possibly related to bandwidth or server issues, but nothing significant.

The scalability of the solution is quite good and I would rate it as an eight out of ten. While it hasn't yet missed our requirements, we haven't pushed it to its limits. We don't deal with edge cases that demand extreme scalability.

Our clients typically include large multinational or state enterprises, as well as national companies in Indonesia.

Azure's setup feels friendlier and easier compared to AWS, making it simpler to understand and use. I would rate the easiness of the setup as an eight out of ten.

Deployment typically takes a few days to a few weeks to build prototypes and get familiar with available features. It is not too short to explore challenging cases, yet not too long to maintain efficiency.

I would rate the costliness of the solution as a nine out of ten.

I would recommend Azure Machine Learning Studio to others if they have enough resources to handle it. However, it is not a plug-and-play solution; there is a learning curve that needs to be addressed.

Overall, I would rate Azure Machine Learning Studio as an eight out of ten.

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 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.

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.

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.