Guides and hiring-demand analysis for important AI skills.
7 published insightsLearn to separate model proposals from authorized actions, bind approvals to exact arguments and handle retries using a runnable in-memory tool example, solved exercises and a sandbox project.
Practise releasing a complete AI service, measuring end-to-end latency, monitoring failures and planning rollback through synthetic measurements, exercises and a testable deployment mini-project.
Build practical Python and SQL skills with an original record-cleaning example, a runnable SQL query, three explained exercises and a mini-project with clear completion checks.
Learn to design honest ML evaluations, calculate threshold metrics, investigate leakage and compare error slices through a worked example, exercises and a reproducible evaluation project.
Practise reliable document extraction with explicit unknown values, structural and evidence checks, a local validator, three solved exercises and a provider-independent mini-project.
Build a retrieval baseline, compare lexical and vector search, preserve access boundaries and evaluate evidence coverage with an original local example, exercises and a mini-project.
The AI skills that form a durable hiring stack: software engineering, data and ML foundations, LLM evaluation, production operations and human judgment.