Microsoft Azure Machine Learning Studio Reviews

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.

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.

My primary use is for machine learning applications.

The AutoML is helpful when you're starting to explore the problem that you're trying to solve. It helps automate some of the applications of the algorithm.

The AutoML feature is very basic and they should improve it by using a more robust algorithm. It lacks deep learning type algorithms but works great for the basic classification and regression models.

I have been using the Azure Machine Learning Studio on and off, or a few months. I have not used it consistently for a significant period of time.

From my experience over the past few months, I've found it to be pretty stable. I don't know how stable it would be if operationalized.

From my experience, I think that it's scalable.

Technical support is pretty good at answering questions, and the documentation is pretty clear to understand.

Compared to their big competitor, it's much easier to set up.

I work with a data architect who does the setup. I have not personally had to do it.

We are in the process of deciding which machine learning solution we want to use. I have been dabbling with Azure and we're deciding whether to implement it versus another cloud platform.

I haven't done any research into what features they have on their roadmap.

Overall, I think that this is a comparable product.

I would rate this solution a seven out of ten.

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.