Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course provides an advanced exploration of MLOps, focusing on how enterprise machine learning systems are scaled, governed, optimized, monitored, and continuously improved in production. Through hands-on demonstrations and practical exercises, learners will use Amazon SageMaker, SageMaker Feature Store, SageMaker Clarify, SHAP, LIME, distributed training, hyperparameter tuning, monitoring dashboards, and active learning techniques to create dependable production workflows. By the end of this course, learners will be able to: - Compare managed ML platforms with self-managed infrastructure - Design scalable feature stores and distributed training processes - Implement model approval, governance, bias, and fairness checks - Monitor model performance, data quality, and drift - Apply continuous learning and cost optimization strategies This course is designed for experienced ML engineers, MLOps engineers, AI engineers, platform engineers, and data scientists who want to build and operate enterprise-scale ML systems. Prior knowledge of MLOps, cloud platforms, Kubernetes, model deployment, and monitoring is recommended.

Syllabus

Managed ML Platforms and Scaling ML Infrastructure

Build a strong foundation in managed ML platforms and scalable ML infrastructure by learning how production ML workflows are migrated, orchestrated, and optimized. Explore how managed platforms reduce operational overhead, support feature reuse, and enable distributed training. Apply these concepts through hands-on exercises to build reliable and scalable production ML systems.

Model Governance, Explainability, and Monitoring in Production

Build practical skills in model governance, explainability, and production monitoring by learning how ML systems are approved, audited, explained, and monitored in production environments. Explore how governance workflows, bias detection, explainability methods, failure debugging, and drift monitoring improve trust, compliance, and reliability. Apply these concepts through hands-on activities to manage model approvals, detect fairness issues, explain predictions, debug failures, and monitor production ML performance.

Advanced Production Patterns and Continuous Learning

Develop practical expertise in production patterns and continuous learning strategies for enterprise ML systems. Learn how ensemble architectures, model routing, online learning, active learning, cost optimization, and SLOs support reliable model operations at scale. Apply these concepts to build multi-model systems, implement incremental retraining, optimize production costs, and define operational dashboards for production-ready ML systems.

Course Wrap-Up and Assessment

This final module assesses your ability to design reliable enterprise fraud detection systems using managed ML, governance, monitoring, explainability, retraining, and cost optimization.