What is your primary use case for Microsoft Azure Machine Learning Studio?

Microsoft Azure Studio allows you to connect to multiple databases and do analysis.

I use the solution to create a data flow and map all the databases or users.

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.

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.

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.

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.

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

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

We use the solution to develop prompt flows.

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.

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.

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

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

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.

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.

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 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.

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.

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.

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'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.

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

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

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

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.

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

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.

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 primarily use this solution for data analytics and model building.

My primary use is for machine learning applications.

We primarily use the solution for data science.

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.

Azure Machine Learning Studio works with our ERP solution.

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

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.

Exploration of connections between biodata and psychometric test results.