Book Image

Agile Machine Learning with DataRobot

By : Bipin Chadha, Sylvester Juwe
Book Image

Agile Machine Learning with DataRobot

By: Bipin Chadha, Sylvester Juwe

Overview of this book

DataRobot 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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Code in Action
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Foundations
Section 1: Foundations
Free Chapter
2
Chapter 1: What Is DataRobot and Why You Need It?
Chapter 1: What Is DataRobot and Why You Need It?
Technical requirements
Data science processes for generating business value
Challenges associated with data science
DataRobot architecture
Navigating and using DataRobot features
Addressing data science challenges with DataRobot
Summary
3
Chapter 2: Machine Learning Basics
Chapter 2: Machine Learning Basics
Data preparation
Data visualization
Machine learning algorithms
Performance metrics
Understanding the results
Summary
4
Chapter 3: Understanding and Defining Business Problems
Chapter 3: Understanding and Defining Business Problems
Understanding the system context
Understanding the why and the how
Getting to the root of the business problem
Defining the ML problem
Determining predictions, actions, and consequences for Responsible AI
Operationalizing and generating value
Summary
Further reading
5
Section 2: Full ML Life Cycle with DataRobot: Concept to Value
Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6
Chapter 4: Preparing Data for DataRobot
Chapter 4: Preparing Data for DataRobot
Technical requirements
Connecting to data sources
Aggregating data for modeling
Cleansing the dataset
Working with different types of data
Engineering features for modeling
Summary
7
Chapter 5: Exploratory Data Analysis with DataRobot
Chapter 5: Exploratory Data Analysis with DataRobot
Data ingestion and data cataloging
Data quality assessment
EDA
Setting the target feature and correlation analysis
Feature selection
Summary
8
Chapter 6: Model Building with DataRobot
Chapter 6: Model Building with DataRobot
Configuring a modeling project
Building models and the model leaderboard
Understanding model blueprints
Building ensemble models
Summary
9
Chapter 7: Model Understanding and Explainability
Chapter 7: Model Understanding and Explainability
Reviewing and understanding model details
Assessing model performance and metrics
Generating model explanations
Understanding model learning curves and trade-offs
Summary
10
Chapter 8: Model Scoring and Deployment
Chapter 8: Model Scoring and Deployment
Scoring and prediction methods
Generating prediction explanations
Analyzing predictions and postprocessing
Deploying DataRobot models
Monitoring deployed models
Summary
11
Section 3: Advanced Topics
Section 3: Advanced Topics
12
Chapter 9: Forecasting and Time Series Modeling
Chapter 9: Forecasting and Time Series Modeling
Technical requirements
Conceptual introduction to time series forecasting modeling
Defining and setting up time series projects
Building time series forecasting models and understanding their model outcomes
Making predictions with time series models
Advanced topics in time series modeling
Summary
13
Chapter 10: Recommender Systems
Chapter 10: Recommender Systems
Technical requirements
A conceptual introduction to recommender systems
Approaches to building recommender systems
Defining and setting up recommender systems in DataRobot
Building recommender systems in DataRobot
Making recommender system predictions with DataRobot
Summary
14
Chapter 11: Working with Geospatial Data, NLP, and Image Processing
Chapter 11: Working with Geospatial Data, NLP, and Image Processing
Technical requirements
A conceptual introduction to geospatial, text, and image data
Defining and setting up multimodal data in DataRobot
Building models using multimodal datasets in DataRobot
Making predictions using a multimodal dataset on DataRobot
Summary
15
Chapter 12: DataRobot Python API
Chapter 12: DataRobot Python API
Technical requirements
Accessing the DataRobot API
Using the DataRobot Python client
Building models programmatically
Making predictions programmatically
Summary
16
Chapter 13: Model Governance and MLOps
Chapter 13: Model Governance and MLOps
Technical requirements
Governing models
Addressing model bias and fairness
Implementing MLOps
Notifications and changing models in production
Summary
17
Chapter 14: Conclusion
Chapter 14: Conclusion
Finding out additional information about DataRobot
Future of automated machine learning
Future of DataRobot
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18
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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.

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