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A guided approach to RapidMiner Studio
To accelerate your work, RapidMiner provides tools that guide you through the process of preparing data, building models, and deploying those models.
- For interactive data preparation, try Turbo Prep.
- For automated machine learning, try Auto Model.
- For one-click deployment of models, try Deployments.
By using these tools, subject matter experts get simplicity, new data scientists can learn best practices, and expert data scientists will boost their productivity.
Turbo Prep: preparing your data
Turbo Prep is designed to make data preparation easier. It provides a user interface where your data is always visible front and center, where you can make changes step-by-step and instantly see the results, with a wide range of supporting functions to prepare your data for for model-building or presentation.
Auto Model: building your model
Auto Model accelerates the process of building and validating models. It addresses three large classes of problems:
- Prediction
- Clustering
- Outliers
Within the Prediction category, you can solve both classification and regression problems. Auto Model helps you to evaluate your data, provides relevant models for the solution of your problem, and helps you to compare the results for these models, once the calculations are completed.
Auto Model not only helps you to get results; it also helps you to understand those results, even for models such as Deep Learning where the inner logic may be hard to understand.
Deployments: deploying your model
To realize the full value of your models, you have to put them into production. From within Auto Model, you can deploy a model with a single click!
A deployment is a collection of models describing the same input data. In its simplest form, it lives in a repository and scores data (e.g., makes predictions), but it can do much more!
- A deployment organizes your models and keeps essential data together in one place (e.g., for compliance with regulations such as GDPR).
- A deployment tracks the performance of your models over time, alerting you to drift and bias.
- A deployment can be shared by a group collaborating on a common project.
- A deployment provides web services, so that you can integrate it with your other software.
The basic philosophy is: the more models you can get into production, the better. Therefore, deployment should be as easy as possible. Why let your models go to waste?
