Agile Machine Learning with DataRobot

Bipin Chadha, Sylvester Juwe

• Publisher: Packt Publishing
• Year: 2021
• eBook ISBN: 9781801078641
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from dataKey Features• Get well-versed with DataRobot features using real-world examples• Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle• Make use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML modelsBook DescriptionDataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization.You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities.By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.What you will learn• Understand and solve business problems using DataRobot• Use DataRobot to prepare your data and perform various data analysis tasks to start building models• Develop robust ML models and assess their results correctly before deployment• Explore various DataRobot functions and outputs to help you understand the models and select the one that best solves the business problem• Analyze a model's predictions and turn them into actionable insights for business users• Understand how DataRobot helps in governing, deploying, and maintaining ML modelsWho this book is forThis book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. The book assumes a basic understanding of machine learning.

Section 1: Foundations

This section will cover some basic but critical items for the success of an ML project. Whether you are just starting or are an experienced data scientist, you will find some topics that you might not be familiar with or have skipped in the past.

This section comprises the following chapters:

• Chapter 1, What Is DataRobot and Why You Need It
• Chapter 2, Machine Learning Basics
• Chapter 3, Understanding and Defining Business Problems

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

Technical requirements

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 processes for generating business value

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 of data science and new ones are being discovered every day. Some example applications are listed here:

• Predicting which customer is most likely to buy a product
• Predicting which customer will come back
• Predicting what a customer will want next
• Predicting which customer might default on a loan
• Predicting which customer is likely to have an accident
• Predicting which component of a machine might fail
• Forecasting how many items will be sold in a store
• Forecasting how many calls the call center will receive tomorrow
• Forecasting how much energy will be consumed next month

Figure 1.1 shows a high-level process that describes how a data science project might go from concept to value generation:

Figure 1.1 – Typical process steps with details about what happens during each step

Following these steps is critical for a successful machine learning project. Sometimes these steps get skipped due to deadlines or issues that inevitably surface during development and debugging. We will show how using DataRobot helps you avoid some of the problems and ensure that your teams are following best practices. These steps will be covered in great detail, with examples, in other chapters of this book, but let's get familiar with them at a high level.

Problem understanding

This is perhaps the most important step and also the step that is given the least attention. Most data science projects fail because this step is rushed. This is also the task where you have the least methods and tools available from the data science disciplines. This step involves the following:

• Understanding the business problem from a systemic perspective
• Understanding what it is that the end users or consumers of the model's results expect
• Understanding what the stakeholders will do with the results
• Understanding what the potential sources of data are and how the data is captured and modified before it reaches you
• Assessing whether there are any legal concerns regarding the use of data and data sources
• Developing a detailed understanding of what various features of the datasets mean

Data preparation

This step is well known in the data science community as data science teams typically spend most of their time in this step. This is a task where DataRobot's capabilities start coming into play, but not completely. There is still a lot of work that the data science or data engineering teams have to do using SQL, Python, or R. There are also many tasks in this step that require a data scientist's skill and experience (for example, feature engineering), even though DataRobot is beginning to provide capabilities in this area. For example, DataRobot provides a lot of useful data visualizations and notific...

Table of contents

• Agile Machine Learning with DataRobot
• Contributors
• Preface
• Section 1: Foundations
• Chapter 1: What Is DataRobot and Why You Need It?
• Chapter 2: Machine Learning Basics
• Chapter 3: Understanding and Defining Business Problems
• Section 2: Full ML Life Cycle with DataRobot: Concept to Value
• Chapter 4: Preparing Data for DataRobot
• Chapter 5: Exploratory Data Analysis with DataRobot
• Chapter 6: Model Building with DataRobot
• Chapter 7: Model Understanding and Explainability
• Chapter 8: Model Scoring and Deployment
• Section 3: Advanced Topics
• Chapter 9: Forecasting and Time Series Modeling
• Chapter 10: Recommender Systems
• Chapter 11: Working with Geospatial Data, NLP, and Image Processing
• Chapter 12: DataRobot Python API
• Chapter 13: Model Governance and MLOps
• Chapter 14: Conclusion
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