This book requires that you have access to DataRobot. DataRobot is a commercial piece of software, and you will need to purchase a license for it. Most likely your organization has already purchased DataRobot licenses, and your administrator can set up your account on a DataRobot instance and provide you with the appropriate URL to access DataRobot.

A trial version is available, at the time of the writing of this book, that you can access from DataRobot's website: https://www.datarobot.com/trial/. Please be aware that the trial version does not provide all of the functionality of the commercial version, and what it provides may change over time.

Data science is an emerging practice that has seen a lot of hype. Much of what it means is under debate and the practice is evolving rapidly. Regardless of these debates, there is no doubt that data science methods can provide business benefits if used properly. While following a process is no guarantee of success, it can certainly improve the odds of success and allow for improvement. Data science processes are inherently iterative, and it is important to not get stuck in a specific step for too long. People looking for predictable and predetermined timelines and results are bound to be disappointed. By all means, create a plan, but be ready to be nimble and agile as you proceed. A data science project is also a discovery project: you are never sure of what you will find. Your expectations or your hypotheses might turn out to be false and you might uncover interesting insights from unexpected sources.

There are many known applications...

It is no secret that getting value from data science projects is hard, and many projects end in failure. While some of the reasons are common to any type of project, there are some unique challenges associated with data science projects. Data science is still a relatively young and immature discipline and therefore suffers from problems that any emerging discipline encounters. Data science practitioners can learn from other mature disciplines to avoid some of the mistakes that others have learned to avoid. Let's review some of the key issues that make data science projects challenging:

• Lack of good-quality data: This is a common refrain, but this is a problem that is not likely to go away anytime soon. The key reason is that most organizations are used to collecting data for reporting. This tends to be aggregate, success-oriented information. Data needed for building models, on the other hand, needs to be detailed and should capture...

DataRobot is one of the most well-known commercial tools for automated ML (AutoML). It only seems appropriate that the technology meant to automate everything should itself benefit from automation. As you go through the data science process, you will realize that there are many tasks that are repetitive in nature and standardized enough to warrant automation. DataRobot has done an excellent job of capturing such tasks to increase the speed, scale, and efficiency of building and deploying ML models. We will cover these aspects in great detail in this book. Having said that, there are still many tasks and aspects of this process that still require decisions, actions, and tradeoffs to be done by data scientists and data analysts. We will highlight these as well. The following figure shows a high-level view of the DataRobot architecture:

Figure 1.2 – Key components of the DataRobot architecture

The figure shows five key layers of the...

Now that you have some familiarity with the core functions, let's take a quick tour of what DataRobot looks like and how you navigate the various functions. This section will introduce DataRobot at a high level, but don't worry: we will get into details in subsequent chapters. This section is only meant to familiarize you with DataRobot functionality.

Your DataRobot administrator will provide you with the appropriate URL and a username and password to access your DataRobot instance. In my experience, Google Chrome seems to work best with DataRobot, but you can certainly try other browsers as you see fit.

Note

Please note that the screens and options you see depend on the products you have the license for and the privileges granted to you by your admin. For most part, it will not affect the flow of this book. Since we will be focusing on the ML development core of DataRobot, you should be able to follow along.

So, let&apos...

Now that you know what DataRobot offers, let's revisit the data science process and challenges to see how DataRobot helps in addressing these challenges and why this is a valuable tool in your toolkit.

Lack of good-quality data

While DataRobot cannot do much to address this challenge, it does offer some capabilities to handle data with quality problems:

• Automatically highlights data quality problems.
• Automated EDA and data visualization expose issues that could be missed.
• Handles and imputes missing values.
• Detection of data drift.

Explosion of data

While it is unlikely that the increase in the volume and variety will slow down any time soon, DataRobot offers several capabilities to address these challenges:

• Support for SparkSQL enables the efficient pre-processing of large datasets.
• Automatically handles categorical data encodings and selects appropriate model blueprints.
• Automatically...

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...