With its adaptability to real time events and data sorting capabilities, DataRobot has become a popular software tool used by data analysts, data scientists, and software developers. This highly innovative tool makes data manipulation and modeling time efficient and simple. The Data centric pipelines offer easy manipulation and flexibility when creating machine learning models. DataRobot allows the user to build various types of models for each AI use case, compare them based on accuracy, and provide recommendations for the best fit model.
This is an example of the use case for issuing loans. The data included in this set is comprised of categories such as homeownership, line of credit, annual income… etc. Once the data is loaded, the user must run a data quality assessment to find the outliers and identify any target leakage from the data set. Once the target leakage is detected, the user can then filter out and include data that is most relevant and best suited for using the feature importance option.
Feature importance represented as a green bar for each feature indicates the level of correlation that the category of data has to the target. In the image above, the loan_status category is indicated to have target leakage. The user can then use the Feature list option to narrow down the list of categories being inputted into creating the model. After the data is filtered, DataRobot will suggest the best performing model to begin prediction analysis.
After the models are developed, the user can view the blueprint for each of the specific models to ensure that the model is best fit for their train of analysis. Within the blueprint the user can see the components of data that are being inputted into creating the models. There are multiple types of variables such as Categorical, Numeric, Time Series, and Text Variables. In this example, there are only three of the variables shown as these are the only ones filtered through in the feature list from above. The Blueprint shows the overview of the pre-processing of the raw data before entering the model.
Another component of DataRobot is Feature discovery. With this you can combine various data sets while comparing patterns with the use of modeling experiments. The main modeling experiment choices consist of Multi labeling, Regression, Classification, and Anomaly detection. Along with these features, DataRobot’s MLOP’s provides a quick and easy space to create, deploy, and manage the models that are sent to the production stage. MLOP’s also allows the user to monitor data drift and unexpected changes to the data. The changes occurring to the inputted data and manipulated data can be verified through the blueprint feature as mentioned above.
A key component of DataRobot’s features is the Automated time series which develops forecasting models through the use of Artificial Intelligence. Once a forecasting model is deployed, you can make sure that the model created, stays up to date as there are changes in the trend of the data. Using the Automated time series feature, DataRobot users can perform predictive analysis for many different use cases for various business needs.
DataRobot’s many data filtering and modeling features allow data scientists to optimize time when conducting prediction analysis. The models created by the user contain detailed analysis of the data, helping the user identify the best solution to their target issue. This tool’s quick statistical modeling and data manipulation techniques have paved the way for intuitive understanding of Machine Learning through AI.