AI Practitioner Handbook: A Comprehensive Guide to Delivering AI Projects, Straight from AI Singapore’s Engineers
Are you a new AI engineer embarking on your first AI project? Or maybe you’ve done an AI project or two, and it’s time to figure out how to streamline your workflow. If so, the AI Practitioner Handbook can help you navigate the complex world of AI project execution. In this blog post, we’ll introduce you to the handbook, discuss who it’s for, how you would benefit from reading it, and guide you on how to use it in conjunction with other resources.
For engineers, by engineers
The AI Practitioner Handbook is primarily designed for AI engineers who are deploying their first AI project into production, or those who are looking to improve their engineering practices. It’s also an excellent resource for managers who want to understand the end-to-end process of executing AI projects. The book provides practical guidance and best practices based on the experiences of AI Singapore’s Innovation team, who have delivered over 60 AI Minimum Viable Products (MVPs) under the 100E programme, with the help of apprentices from the AI Apprenticeship Programme.
What sets this book apart is that it’s written by engineers from AI Singapore, based on our own experience of running AI projects. This first-hand knowledge ensures that the book’s content is grounded in real-world scenarios and practical applications.
Moreover, the AI Practitioner Handbook is a live document that will be updated based on readers’ feedback, for more comprehensive coverage in the future. This means that the book will continue to evolve and stay relevant, making it a long-lasting and useful resource for AI practitioners.
How can this book help you?
Delivering a successful AI project goes beyond building models in Jupyter notebooks. It involves a series of steps, including data cleaning and transformation, followed by building, training, and testing the models, and finally deploying the model. Moreover, real-world AI projects require collaborative work among developers, which adds another layer of complexity. The AI Practitioner Handbook covers these aspects in detail and focuses on best practices to ensure successful project outcomes.
By reading this book, you’ll gain insights into the real-world challenges faced during AI project execution and learn how to overcome them. The book will also help you quickly become productive and understand how to manage the complexities of an AI project from inception to completion.
What’s in the book?
The AI Practitioner Handbook is organised into eight chapters, each focusing on a specific aspect of AI project execution:
- Pre-project Phase: Learn about project scoping, data readiness assessment, and initial tech stack selection.
- Project Management & Technical Leadership: Discover insights on project lifecycle, managing interactions with end-users, and leading a team of AI developers.
- Collaborative Development Platforms: Understand how to facilitate a team of developers working together on a single codebase, code quality, and continuous integration.
- Literature Review: Systematise the process of reviewing literature relating to a problem domain and find go-to references for typical AI problems.
- Data Management, Exploration & Processing: Delve into data engineering, data lineage, data versioning, exploratory data analysis, and feature engineering.
- Modelling: Learn about model training, evaluation, error analysis, explainability, interpretability, and machine learning risks.
- Solution Delivery: Explore deployment practices for end-to-end solutions, including incremental and continuous deployment.
- Documentation & Handover: Understand the process of knowledge transfer to technical or non-technical teams taking over an AI solution.
How to use this book
The AI Practitioner Handbook is designed to cater to different reading styles. When read end-to-end, the chapters will cover the typical AI project lifecycle, providing a comprehensive understanding of the entire process. Alternatively, book sections can be read in a standalone manner. Each section is written in a question-and-answer format, making it easy to find relevant information quickly.
The AI Practitioner Handbook complements other resources by focusing on the practical aspects of delivering AI projects. To get the most out of this book, use it alongside resources that cover AI algorithms, techniques, and research that further build upon your AI fundamentals. By doing so, you’ll gain a comprehensive understanding of both the theoretical and practical aspects of AI project execution.
What our reviewers say
“Whether your role in an AI project is that of a technical lead, AI model implementor, data manager, domain or business function expert, or business-side project manager, this handbook will accelerate your learning curve for understanding the end-to-end aspects of the AI project.”
– Steven Miller, Professor Emeritus of Information Systems, Singapore Management University and co-author of Working with AI, MIT Press
“This is a fantastic book because it focuses on an often overlooked aspect of ML education—the actual problems, people and teams you deploy it for. It’s a great resource for anyone who wants to successfully put the theory of ML into practice. BAM!“
– Josh Starmer, Founder and CEO at StatQuest
“AISG’s release of the AI Practitioner Handbook as a practical and credible guide to accelerate the learning curve of incoming AI scientists and engineers is a generous service to Singapore’s growing AI community.”
– Jason Tamara Widjaja, Global AI Lead at a multinational biopharmaceutical company
In conclusion, the AI Practitioner Handbook is an essential resource for AI engineers and managers looking to execute AI projects successfully. With its unique focus on real-world experiences from AI Singapore engineers and its commitment to continuous improvement through reader feedback, this book will help you quickly become productive and ensure your AI projects are a resounding success. Make sure to use it in conjunction with other resources to gain a well-rounded understanding of AI project execution, and stay up-to-date with the latest updates as the handbook continues to evolve.