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AI Practitioner Handbook
Welcome to AI Singapore’s AI Practitioner Handbook
1. Pre-project Phase
How can business challenges be translated into AI problems?
What are some data considerations when framing an AI project?
What are the considerations for reducing technical debt?
How can an engineer assess a client’s AI readiness?
2. Project Management & Technical Leadership
How can I build an effective AI development team?
How can I cultivate a cohesive AI development team?
What kind of engineering principles can I set for my development team?
How might we simplify and translate technical jargon?
3. Collaborative Development Platforms
What are the key platforms required for collaborative ML development?
What are some considerations in setting up a project repository?
4. Literature Review
What are some of the factors to consider during literature review?
5. Data Management, Exploration & Processing
Which data storage options are suitable for the project?
Is there a systematic structure for performing exploratory data analysis?
What are some ways to do EDA for CV tasks?
What are the various data split strategies?
How do we make data splits repeatable?
What are some scenarios of bias, unfairness, data leakage in data splits?
How do I build a basic end-to-end workflow?
How do I enhance my end-to-end workflow?
How can I reduce the risks of data poisoning and data extraction?
6. Modelling
What are some internal and external considerations when selecting evaluation metrics?
How can I maximise model reproducibility?
How do I assess model robustness?
How do I select classification metrics?
What are some common CV evaluation metrics?
What are some metrics for Named Entity Recognition?
How can we evaluate time-series classification models?
How can we provide post-hoc explanations for black-box AI models?
7. Solution Delivery
How can I better understand the client’s deployment requirements?
How can we build a minimum viable configuration for CI/CD automation?
8. Documentation & Handover
What are some good practices in documenting system architecture and processes?
How To Contribute
How should I write a section using Markdown?
How should I write a section using Jupyter notebooks?
How do I review content?
Cite This Book
How Was Your Experience?
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