We used this solution for defining new predictive models, such as recommendation systems, but also price elasticity models for fraud detection, and the classification of customers.

We are not using this solution regularly. We are now using Azure Databricks.

The use cases of this product are primarily for the BFSI; digitization and building machine learning models that provide recommendations for creating analytical insights from extracted data. We also do Jupyter Notebook authoring. We are partners with Microsoft and I'm a practice director.  

We primarily use the solution for our projects. We're currently adopting it for a competitive, new initiative. We create a lot of products related to AI inside the organization as needed for business cases and different business deals. We use it for data extraction and language processing. 

We primarily use the solution for sales forcasting and for creating a pipeline in Azure. We are publishing the pipeline from Azure DevOps, and through the AML endpoint so that the pipeline will run one after the other models. These predictions will be stored and we can visualize everything. 

Microsoft Azure Machine Learning Studio can be used for a variety of machine learning tasks, including deployment and creation of new components.

I've had experience working for two distinct companies. My previous employer operated in the telecom domain, primarily focused on telecom-related projects. In my current role, which is in the shipping domain, we primarily manage shipping cases. Furthermore, our current work predominantly revolves around a machine learning platform implemented on storage systems.

I usually order a machine for training my models. I build the machine myself to include various images for working with Python or IR. I upload my data and scripts to the cloud and run the training process.

We use Microsoft Azure Machine Learning Studio to train our models and for data labeling.

Our use cases  involve customer segmentation for targeted marketing, where I use machine learning to identify potential customers interested in a new product. Another is a recommendation system on our company website, where I use machine learning to suggest additional products to customers based on their browsing or purchase history. Lastly, there is pricing estimation, where I use machine learning to predict the price of an item or article.

My company uses Microsoft Azure Machine Learning Studio to help our company's customers view AI solutions.

My company's clients' use cases will be that they use the solution to feed information to the system about their customers who purchase from them. The solution also helps one to combine products to engage in cross-selling and upselling activities while keeping track of customer lifetime value. The solution also helps its users with the pricing simulation part to figure out what prices are good for the business and maximize the closing of the sale.

Developing and operationally implementing a powerful lead scoring model for a major Multiufamily developer and operator of apartment properties throughout major western states. The work included 3 years of data across over 60 properties with more than 500,000 leads and 3 million transactions.

We used this product as implementers. For example, a client wanted to use the Azure stack, specifically Azure Machine Learning Studio. Microsoft was a consultant on the project, and we were the implementation partner.

There are actually two tools. One is Azure Machine Learning Designer (which used to be called Azure Machine Learning Studio), and the other is Azure Machine Learning. Designer is a drag-and-drop interface, primarily for those without extensive coding expertise.

Azure Machine Learning has become the de facto product, and it allows you to write code and provides numerous components for building machine learning models.

The first time that I used this tool was in a project related to bike usage in the city of Boston. This project was part of a course that I concluded some months ago. In this project I used components to read data, for exploratory analysis, for steps of data munging, to split data, select hyperparameters, and some machine learning algorithms. In some steps I needed to insert R modules to apply some data transformation.

The target of this exercise was to predict bike usage in a day.

We use Microsoft Azure Machine Learning Studio to generate predictive sales analytics and determine customer behavior.

Microsoft Azure Machine Learning Studio can be used for developing models, such as predicting energy usage, as I did for my bachelor's project, where I predicted future energy usage for a city in Norway. The solution can also be used for classification tasks, such as identifying objects in images.

The use cases actually depend on the client's requirements.

We have been working with multiple clients so they have their own use cases, they have their own problem areas, and based on their use cases, we use that platform.

One of the use cases is dealing with dealer churn.

I use a combination of Microsoft Azure Machine Learning Studio and Azure Databricks. I mostly use Azure Databricks for building a machine learning system. There are several workflows for a machine learning tuning system that involves data pre-processing, quick modeling pipelines that execute within a couple of seconds, and complex model pipelines, such as hyperparameters. Additionally, there is a setting to set different AutoML parameters. 

For the training and evaluation phase of the whole machine learning system, I use MLflow, for a testing system and a model serving system, which is one core component of Databricks. I use it for Model Register and it allows me to do many things, such as registering model info, logs, and evaluation metrics.

We use the solution to develop prompt flows.

We're mainly using Microsoft Azure Machine Learning Studio to run experiments on our data for predictive analytics.

We use Microsoft Azure Machine Learning Studio when we need to connect with the customer's data. We can connect easily, and fast, and test and train quickly. We have quick results.

In terms of use case, we implement Microsoft Azure Machine Learning Studio using Python libraries, so basically, we have a centralized studio where we just have to drag and drop features and create the model out of the data that we have. Microsoft Azure Machine Learning Studio is pretty easy to use even for people who don't know much about coding. They just need to know the features and libraries, so it's similar to Tableau and Alteryx because users can drag and drop features to create models or pipelines. We create and deploy pipelines through Microsoft Azure Machine Learning Studio.

Our customers use the solution for its automated machine-learning features.

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

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.

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

We primarily use this product for its price elasticity and the product mix on offer.

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

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.

This solution can be used for data pre-processing, interactive data analysis, automated training, and pre-processing pipelines.

My primary use is for machine learning applications.

We primarily use this solution for data analytics and model building.

We initially moved to this solution because our company needed to complete a system upgrade. We had to move the Db2 data to a AS400 system. 

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.

My primary use case is for supervised and unsupervised learning models.

I use the solution for learning purposes for the most part.

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.

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.

in-house translation, time series and computer vision applications;  create models from scratch and just play around with data visualization.

I am using Microsoft Azure Machine Learning Studio for my personal use.

Azure Machine Learning Studio works with our ERP solution.

Exploration of connections between biodata and psychometric test results.

I have used it to deploy predictive models in the healthcare sector.

We primarily use the solution for data science.

My  primary use of ML Studio is to experiment with different algorithms and learn the techniques of machine learning. In the meantime, I have developed a few models related to finance. One of the predictive models I designed was an Invoice Discrepancy Prediction model using a Multiclass Neural Network algorithm. This model predicts if an invoice will have a variance of some sort when checked against the purchase order, before the payments are to be processed.