Years ago, I had to do a huge financial analysis for my company to predict whether pay increases could reduce employee churn, training costs, productivity, and overall employee moral. I remember running and testing multiple models for weeks, all concluding that there would be a savings. My Director was an incredible guy and asked me to go back and check them one more time before we decided to bump wages for a few hundred employees. I returned and ran the numbers again… with the same results.
I walked my Director through the models. He looked up and asked, “Would you bet your job on this?”… he was serious. “Yes.” We subsequently raised the minimum pay of our employees and the cost savings doubled over the course of the year. My models predicted the right answer, but were way off on the overall impact. At the time, that was the best I could do given Microsoft Access and Excel.
Had I had the computing power and machine learning capabilities available today, I would have had an answer in seconds, and an accurate prediction of the cost savings with minimal error. DataRobot would have been nothing short of a miracle.
DataRobot automates the entire modeling lifecycle, enabling users to quickly and easily build highly accurate predictive models. The only ingredients needed are curiosity and data — coding and machine learning skills are completely optional!
DataRobot is a platform for Data Science Apprentices, Business Analysts, Data Scientists, Executives, Software Engineers, and IT Professionals to create, test, and improve data models quickly and easily. Here’s the overview video:
The process for utilizing DataRobot is simple:
- Ingest your data
- Select the target variable
- Build hundreds of models in one click
- Explore top models and get insights
- Deploy the best model and make predictions
According to DataRobot, their Advantages Include:
- Accuracy – While automation and speed usually come at the expense of quality, DataRobot uniquely delivers on all those fronts. DataRobot automatically searches through millions of combinations of algorithms, data preprocessing steps, transformations, features, and tuning parameters for the best machine learning model for your data. Each model is unique — fine-tuned for the specific dataset and prediction target.
- Speed – DataRobot features a massively parallel modeling engine that can scale to hundreds or even thousands of powerful servers to explore, build and tune machine learning models. Large datasets? Wide datasets? No problem. The speed and scalability of modeling is limited only by the computational resources at DataRobot’s disposal. With all this power, the work that used to take months is now finished in just hours.
- Ease-of-Use – The intuitive web-based interface allows anyone to interact with a very powerful platform, regardless of skill-level and machine learning experience. Users can drag-and-drop then let DataRobot do all the work or they can write their own models for evaluation by the platform. Built-in visualizations, such as Model X-Ray and Feature Impact, offer the deepest insights and a whole new understanding of your business.
- Ecosystem – Keeping up with the growing ecosystem of machine learning algorithms has never been this easy. DataRobot is constantly expanding its vast set of diverse, best-in-class algorithms from R, Python, H20, Spark, and other sources, giving users the best set of analytics tools for predictive challenges. With a simple click of the Start button, users can deploy techniques they have never used before or may not even be familiar with.
- Rapid Deployment – The best predictive models have little to no organizational value unless they are rapidly operationalized within the business. With DataRobot, deploying models for predictions can be done with a few mouse-clicks. Not only that, every model built by DataRobot publishes a REST API endpoint, making it a breeze to integrate within modern enterprise applications. Organizations can now derive business value from machine learning in minutes, instead of waiting months to write scoring code and deal with the underlying infrastructure.
- Enterprise-Grade – Now that machine learning impacts an ever-increasing number of business processes, it is no longer optional to treat it as a developer’s tool with minimal security, privacy and business continuity safeguards. In fact, it is critical that a platform for building and deploying models is hardened, can be trusted and integrates well with the ecosystem of technologies within an organization.