Artificial Intelligence Foundations for Practitioners

Foundations in AI help teams turn ideas into reliable products. They connect data quality, model choice, evaluation, and governance. With a solid base, practitioners can move from experiments to real-world impact while keeping risk under control.

Core ideas

• Data quality and scope: Define the task, gather representative samples, check for bias, missing values, and labeling errors; document data sources.
• Model selection and bias: Match the task to a model type and its assumptions; simple baselines can beat fancy tricks. Be aware of bias in data and predictions.
• Evaluation and reliability: Use suitable metrics, proper validation, and calibration to understand both accuracy and reliability across groups.
• Governance and transparency: Record decisions, privacy controls, and explainability plans; share results with stakeholders.

Practical workflow

An effective workflow follows a clear objective, careful data prep, testing, deployment, and ongoing monitoring. At each stage, set expectations and guardrails.

• Start with a small, well-defined pilot.
• Compare a simple baseline to more complex models.
• Build monitoring for drift and performance.
• Plan updates and rollback paths.

Real-world example

Example: predicting customer churn. Begin with logistic regression or a small decision tree as a baseline. Track ROC-AUC for discrimination and calibration for probability estimates, and check fairness across key groups. When drift or performance fall, retrain with fresh data and adjust features.

Collaboration and learning

AI work is a team effort. Communicate results in plain language, keep data policies visible, and involve product, legal, and ethics early and often.

Key Takeaways

• Foundations connect data, models, and governance.
• Start with a solid baseline and proper evaluation.
• Monitoring and updating after deployment.