You might be able to relate to one of the following scenarios:

• You have been given a business problem and a lot of raw data. You want to engineer this data and choose the right metrics to solve this problem.
• Your initial models perform well in offline experiments and you want to deploy them. You have little feedback on how your models are performing after your models are deployed, and you want to figure out a way to quickly detect, debug, and address any issue your models might run into in production.
• The process of developing, evaluating, deploying, and updating models for your team has been mostly manual, slow, and error-prone. You want to automate and improve this process.
• Each ML use case in your organization has been deployed using its own workflow, and you want to lay down the foundation (e.g., model store, feature store, monitoring tools) that can be shared and reused across use cases.
• You’re worried that there might be biases in your ML systems and you want to make your systems responsible!

You can also benefit from the book if you belong to one of the following groups:

• Tool developers who want to identify underserved areas in ML production and figure out how to position your tools in the ecosystem.
• Individuals looking for ML-related roles in the industry.
• Technical and business leaders who are considering adopting ML solutions to improve your products and/or business processes. Readers without strong technical backgrounds might benefit the most from Chapters 1, 2, and 11.

What This Book Is Not

This book is not an introduction to ML. There are many books, courses, and resources available for ML theories, and therefore, this book shies away from these concepts to focus on the practical aspects of ML. To be specific, the book assumes that readers have a basic understanding of the following topics:

• ML models such as clustering, logistic regression, decision trees, collaborative filtering, and various neural network architectures including feed-forward, recurrent, convolutional, and transformer
• ML techniques such as supervised versus unsupervised, gradient descent, objective/loss function, regularization, generalization, and hyperparameter tuning
• Metrics such as accuracy, F1, precision, recall, ROC, mean squared error, and log-likelihood
• Statistical concepts such as variance, probability, and normal/long-tail distribution
• Common ML tasks such as language modeling, anomaly detection, object classification, and machine translation

You don’t have to know these topics inside out—for concepts whose exact definitions can take some effort to remember, e.g., F1 score, we include short notes as references—but you should have a rough sense of what they mean going in.

While this book mentions current tools to illustrate certain concepts and solutions, it’s not a tutorial book. Technologies evolve over time. Tools go in and out of style quickly, but fundamental approaches to problem solving should last a bit longer. This book provides a framework for you to evaluate the tool that works best for your use cases. When there’s a tool you want to use, it’s usually straightforward to find tutorials for it online. As a result, this book has few code snippets and instead focuses on providing a lot of discussion around trade-offs, pros and cons, and concrete examples.