What is our primary use case?
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.
How has it helped my organization?
With this tool we could have all benefits of a cloud environment, such as scalability and access to machine-learning applications. These features are very important when you have large datasets and critical applications.
What is most valuable?
- It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
- When you import the dataset you can see the data distribution easily with graphics and statistical measures.
- Easy to deploy and provide the project like a service.
What needs improvement?
For my project/exercise, this tools was perfect. I would like to see modules to handle Deep Learning frameworks.
For how long have I used the solution?
Less than one year.
What do I think about the stability of the solution?
No issues with stability.
What do I think about the scalability of the solution?
No issues with scalability.
How are customer service and technical support?
I didn’t need to use the support, but this tool has great documentation.
Which solution did I use previously and why did I switch?
Nowadays I use Python (Anaconda and Jupyter Notebook) and R (RStudio) to create my solutions and machine-learning models.
How was the initial setup?
It was very simple and straightforward. It is really simple to start building a project.
What's my experience with pricing, setup cost, and licensing?
There are two kinds of licenses, Free and Standard.
- 100 modules per experiment.
- 1 hour per experiment.
- 10GB storage space.
- Single Node Execution/Performance.
Standard – $9.99/seat/month (probably a data scientist)
- $1 per Studio Experimentation Hour. You will pay according to the number of hours your experiments run.
- Unlimited modules per experiment.
- Up to seven days per experiment, 24 hours per module.
- Unlimited BYO storage space.
- On-premises SQL data processing.
- Multiple Nodes Execution/Performance.
- Production Web API.
What other advice do I have?
You will be able to create your machine-learning project and extract insights from it just by dragging and dropping components and adjusting some parameters. This tool is very user-friendly, so without a lot of programming skills you can build machine-learning projects.
If you need more control over machine-learning modules you will need to add R or Python modules to create a customized machine-learning model.