Production AI Model Development and Ethics
By External Provider
About this course
This comprehensive program provides end-to-end training on the production machine learning lifecycle, designed to take your models from experiment to deployment. You’ll progress from applying feature engineering pipelines with scikit-learn and selecting models through rigorous evaluation, to optimizing PyTorch models with custom training loops and advanced diagnostics. Finally, you will master the principles of responsible AI by creating model cards and auditing systems for ethical compliance. By the end of this course, you will be able to build, tune, and deploy efficient, reliable, and ethical AI solutions. These skills are essential for ML engineers who develop and maintain robust, production-grade machine learning systems.
What you will learn
- Apply custom training loops with callbacks (early-stopping, checkpointing) and diagnose gradient issues using norm and activation analysis.
- Implement feature engineering pipelines for structured and text data, then evaluate ML experiments to select production-ready models.
- Create comprehensive model cards for LLM features that detail intended use, technical limitations, and specific fairness metrics.
- Evaluate AI systems against established ethical guidelines to identify biases and propose actionable mitigation strategies.