Summary
Most data scientists today are bogged down in the implementation details or are implementing suboptimal algorithms. This leaves them with less time to understand the problem and to search for optimal algorithms or their hyperparameters. This book will show you how to take your game to the next level and let the software do the repetitive work.
In this chapter, we covered what a typical data science process is and how DataRobot supports this process. We discussed steps in the process where DataRobot offers a lot of capability and we also highlighted areas where a data scientist's expertise and domain understanding is critical (areas such as problem understanding and analyzing the impacts of deploying a model on the overall system). This highlights an important point in that success comes from the combination of skilled data scientists and analysts and appropriate tools (such as DataRobot). By themselves, they cannot be as effective as the combination. DataRobot enables relatively new data scientists to quickly develop and deploy robust models. At the same time, experienced data scientists can use DataRobot to rapidly explore and build a broader range of models than they would be able to build on their own.
We covered some of the key data science challenges and how DataRobot helps you overcome some of the specific challenges. This should help guide leaders on how to craft the right combination of data scientists and the tools and infrastructure they need. We also covered the DataRobot architecture, its components, and what DataRobot looks like. You got a taste of what you will see when you start using it and where to go to find specific functions and help.
Hopefully, this chapter has shown you why DataRobot could be an important tool in your toolbox regardless of your experience or how comfortable you are with coding. In the following chapters, we will use hands-on examples to show how to use DataRobot in detail and how to move your projects into a higher gear. But before we do that, we need to cover some ML basics in the next chapter.
