What needs improvement with Microsoft Azure Machine Learning Studio?

One area where Azure Machine Learning Studio could improve is its user interface structure. Simplifying the initial information presented upon first use could make it more accessible, especially for users with limited technical skills. Providing only essential information upfront would enhance the user experience and reduce complexity.

The price could be improved.

It would be great if the solution integrated Microsoft Copilot, its AI helper.

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.

The product must improve its documentation.

Improvement will be possible with more machine learning functionalities in Microsoft Azure Machine Learning Studio since, at times, the current accuracy of the solution is not good enough. It would be good if Microsoft Azure Machine Learning Studio could have a generative AI tool similar to ChatGPT.

The regulatory requirements of the product need improvement. Many customers, including government clients, need data processing on the cloud. However, because of these regulatory requirements, I cannot use the website's machine learning and data features. I have to do everything manually, which is very time-consuming. I am trying to save the metadata on the cloud and the people's data on-premises. Microsoft should improve the configuration process. Additionally, access to accessible sources from the mobile console should be available.

The platform’s integration with Apache could be better.

The solution's learning models developed using Python coding are not robust. The AI features need to summarize vast amounts of data into simple models. It must understand all the mathematical parameters and formulas within the models for reliable predictions. They need to work on this particular area. Also, they should provide integration with Microsoft Teams as well.

The initial setup time of the containers to run the experiment is a bit long.

The icons in the solution could be improved to include examples of how to use each container, as sometimes it's unclear which container to choose. It would be helpful to provide examples to understand better which virtual machine or how many courses to use. Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier.

The solution cannot connect to private block storage. It does not allow this connection, which is a pain point. The confidential data needs to be removed from the block, and that becomes a security issue. In Azure Databricks, how we are promoting the models could be easier. The UI in Daabricks is a bit easier. We'd like ML Studio to be streamlined.

The price of the solution has room for improvement.

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.

As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased.

Technical support could improve their turnaround time.

It's not that easy to master the program, it requires some specific learning. If we want to extend the program to include inexperienced users, it can take some time for them to learn the solution. It would be nice if they added GPU solutions. Most of the solutions coming out now are video analytics or edge computing-based and Azure should have that focus.

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.

In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data.

The interface is a bit overloaded.

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 found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system. The developers for this solution have not been as active in improving it as other solutions have had more improvements, such as Databricks. Sometimes there might be some data drifting problems and this is what I am currently working on. For example, when our new data has a drift from the previous old data. I need to first work out a solution. Azure in Databricks or in Azure Machine Learning Studio both works fine. However, the normal data drifting solution is not working that well for the problem that I am facing. I am able to receive the distribution change and numerical metrics changes, but it will not inform me how to fix them.

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.

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.

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.

In terms of improvement, I'd like to have more ability to understand the detailed impact of the variables on the model and their interactions. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" Azure (at least my understanding of it) doesn't provide readily accessible tools to assess from a management perspective the impact of their changing a sinimized, the better.gle value - for instance in closing a lead, decreasing response time by 10%. I recognize that the multivariate algorithms used from decision trees to neural nets do not readily provide the coefficients for each variable ala the older regression modeling approaches. My experience over my 50 years of developing and implementing predictive models has been that more than half the value of modeling lies in improving management's understanding of the process being modeled, often leading to major organization and operational structure changes. More ability to understand the variables impacting the end result being optimized would be very useful.

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.

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.

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 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.

The solution should be more customizable. There should be more algorithms. The solution needs more functionality.

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.

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.

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.

If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice. One of the problems that we had was that you could only execute the model inside the machine learning environment. Comparing this to Databricks, if you create a pipeline, it could be in a notebook and you have all the code and then you can export your notebook to some other tool directly, for example in Jupyter and Spark. If you change tools then you won't lose your assets. I would like to see improvements to make this solution more user-friendly. They need to have some tools, like Apache Airflow, for helping to build workflows. Better tools are needed to bring the data from existing storage into the environment where they can play with it and start to analyze what they already have, on-site. This is what the majority of people would like to do. A feature that would be useful is to have some standard data transportation functions. They have ADF, Azure Data Factory, but it's a little bit heavy to manipulate. If they could have something more user-friendly, like Apache Airflow, it would be very nice.

Operability with R could be improved.