Optimizing Machine Learning Performance

This module focuses on machine learning strategy, covering topics such as ML readiness, risk mitigation, experimental mindset, and setting up a team. It also delves into understanding and communicating change, IP questions, and positioning your company.

Responsible Machine Learning explores AI for good, positive and negative feedback loops, metric design, observing behaviors, and regulatory concerns. It also covers feedback systems affecting you and responsible machine learning review.

Machine Learning in Production & Planning module discusses integrating information systems, time and space complexity in production, retraining the model, logging ML model versioning, and reporting performance to stakeholders. It also includes complexity in production review and machine learning in production and planning review.

This module covers the care and feeding of your machine learning system, including post-deployment challenges, QuAM monitoring and logging, testing, maintenance, updating, separating datastack from production, and dashboard essentials & metrics monitoring.