Microsoft Azure Machine Learning Studio Reviews

Our organization employs people with diverse professional backgrounds. We have sociology, mathematics, and statistics backgrounds. We employ these people within our data science team. They require a certain amount of programming skills.

The good thing about Azure Machine Learning is they have a drag and drop feature. You can use Azure Machine Learning designer for all of your data science teams.

Any non-programmer can adopt it. All he needs is statistics and data analysis skills.                                                                                             

I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer.

I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.

We have been using Azure for three months. We have been exploring it for different use cases. 

I haven't used it long enough to have found any bugs in our current system. If there were bugs I would definitely report it on their website.

We didn't have any problems with the setup. It was pretty straightforward.

It's an easy tool. They have a good level of resources and we are pretty low with resources as far as data science is concerned.

Azure Machine Learning offers an opportunity for those who haven't been introduced to Azure programming. You can use the data analytics and their statistics skills to build and deploy data science solutions that can be beneficial for society and for different organizations.

I would rate it a seven out of ten.

I use it for forecasting solutions, and building, deploying, and managing machine learning models.

One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option. As designers, we have the flexibility to leverage end-to-end features without having to code everything manually. Additionally, the platform provides convenient options for managing email operations. I appreciate the extensible AI feature; it effortlessly generates a report even in the absence of explicit report instructions.

There's room for improvement in terms of binding the integration with Azure DevOps. I find the process somewhat intricate, especially when connecting to the issue-tracking system. Numerous steps and configurations need to be set up before effectively utilizing Azure DevOps. When it comes to the Home Office Machine Learning suite, I believe it would be more beneficial if there were shared capabilities for internet projects.

I have been working with it for one year.

The stability is impeccable. I would rate it ten out of ten.

I would rate its scalability capabilities nine out of ten. Ten users utilize it on a daily basis.

I'm dissatisfied with the technical support; they failed to offer the correct solution. I would rate their expertise four out of ten.

Neutral

The initial setup was fairly straightforward. I would rate it seven out of ten.

The deployment was completed within a week by following the guidebook. The in-house implementation was done by one individual. Maintenance is handled by a single individual who monitors the logs.

Overall, I would rate it seven 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.

We plan to use this solution for everything in business analytics including data harmonization, text analytics, marketing, credit scoring, risk analytics, and portfolio management.

It allows us to do machine learning experiments quickly.

We did not have machine learning solutions or platform earlier.

It's user-friendly, and it's a no-code model development. It's good for citizen data scientists, but also, other people can use Python, R or .NET code.

If you are on Microsoft Cloud, the development and implementation are super easy.

Every tool requires some improvement. They have already improved many things. They had added new features and a new pipeline.

They should have an on-premise version, other than Python and R Studio, which is only good for cloud-based deployments.

If they could have a copy of the on-premise version on Mac or Linux or Windows, it would be helpful.

It should have the flexibility to work o the desktop. They should have a desktop version to work on the platform.

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

It's a stable solution. Microsoft is very stable in general.

It's very scalable because it is using Microsoft cloud compute power.

We want to extend organization-wide, but currently, we are only working on a use case basis.

We have not required help from technical support, but Microsoft technical support comes with it when you subscribe.

Deployment of the tool is simple. Just one click on Microsoft. Once you have procured the license, you can just log in and use it. It's a ready-to-use tool.

When you deploy the solution after analytic development, it depends on the project but it can take anywhere from one month to six months.

Also, depending on the infrastructure, the initial deployment can take one week to a month.

In-house expertise.

The licensing cost is very cheap. It's less than $50 a month would costs for multiple users.

If you want to build a solution quickly without knowing any coding, it's pretty good to start with.

I will take a week to learn, from my experience. For anyone who is interested in trying it, they should start with the free version, which is free for up to 10 gigabytes of workspace.

Just log in and start developing and exploring the tool before onboarding.

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

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.

Our primary use for this solution is for customer service. Specifically, chat responses based on pre-defined questions and answers.

We have reduced the theme size front-line agents by ten percent using the AI elements on chat and email response.

The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses. This reduces our resources and costs.

The user interface that we have is relatively simple.

Some of the terminologies, or the way that the questions are asked, could be stronger. When people use local colloquialisms, it would be better if it understood rather than forwarding it to an agent.

If the frontline efficiencies were improved then we could pass this on to our clients.

Integration with social media would be a valuable enhancement.

I have been using the Microsoft Azure Machine Learning Studio for about eighteen months.

The stability is good and we haven't had any issues.

Scalability for us was fine.

We have about seven hundred users including customer service agents, sales agents, and cell phone account managers. It took us about twelve months to scale to this point, from an initial user base of seventy people, and we do not plan to increase usage further.

We've got an internal IT department and we raised inquiries through them. They speak with whoever they need to in order to resolve the ticket.

The previous solution that we were using was based on the Aspect platform. It was fifteen years old, which is why we reviewed it. We weren't able to offer any kind of AI or omnichannel experience using that platform, as its pure telephony. Anything else that we did was piecemeal. We switched because the platform couldn't offer the support that we needed for our clients.

The initial setup is straightforward.

Our deployment took about six weeks, but that was also integrating the new telephony platform as well. For the AI elements, it was probably around five days.

Once the initial knowledge base was set it it took time to build and get it to where we needed it to be. Until that happens you can't really implement the AI element. This is what took about six weeks, so that it covered all of the inquiries that we wanted.

We started with an on-premises deployment and have moved to the cloud.

We performed most of the implementation on-site by ourselves, but we had some help from a consultant to give us guidance.

My advice to anybody who is implementing this solution is to be prepared to take a slow approach to get the best results.

The biggest lesson that I have learned from using this solution is that the strategic outsourcing contact will need to have a strategy for the next three to five years because the efficiencies that we will be gaining from AI will reduce the requirements on physical staff doing traditional roles. However, the support element will increase. It means that the roles will change and evolve over the next three to five years within the UK contact center based on the deployment of AI.

I think that we probably didn't start from the point that would have benefited us most in terms of the AI. Had we put more research into the front end then there would have been a lot less work during the implementation.

I would rate this solution a six out of ten.

We develop products on the solution. It also provides fraud detection. We use it mainly for IoT to save on the electricity bill for heating in the warehouses.

The product supports open-source tools. The integration with data services is an important feature. We use it in case the data is already available.

The price could be improved.

The tool is mature.

The tool is scalable. We have four users in our organization. We have plans to increase the usage in the future.

Whatever we develop, we deploy from the GUI. The tool can be easily deployed.

We do the deployment in-house.

We have an enterprise contract.

We used Google in the past.

Overall, I rate the solution 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.