Chapter 1: What Is DataRobot and Why You Need It?
Machine learning (ML) and AI are all the rage these days, and it is clear that these technologies will play a critical role in the success and competitiveness of most organizations. This will create considerable demand for people with data science skills.
This chapter describes the current practices and processes of building and deploying ML models and some of the challenges in scaling these approaches to meet the expected demand. The chapter then describes what DataRobot is and how DataRobot addresses many of these challenges, thus allowing analysts and data scientists to quickly add value to their organizations. This chapter also helps executives understand how they can use DataRobot to efficiently scale their data science practice without the need to hire a large staff with hard-to-find skills, and how DataRobot can be leveraged to increase the effectiveness of your existing data science team. This chapter covers various components of DataRobot, how it is architected, how it integrates with other tools, and different options to set it up on-premises or in the cloud. It also describes, at a high level, various user interface components and what they signify.
By the end of this chapter, you will have learned about the core functions and architecture of DataRobot and why it is a great enabler for data analysts as well as experienced data scientists for solving the most critical challenges facing organizations as they try to extract value from data.
In this chapter, we're going to cover the following topics:
- Data science practices and processes
- Challenges associated with data science
- DataRobot architecture
- DataRobot features and how to use them
- How DataRobot addresses data science challenges
