Optimize AI: Plan, Evaluate, and Learn

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Optimize AI: Plan, Evaluate, and Learn

Coursera · Intermediate ·🏭 MLOps & LLMOps ·3mo ago

Key Takeaways

Optimizes AI systems through change and uncertainty using performance data analysis, algorithm evaluation, and continuous-learning strategies

Original Description

Optimize AI: Plan, Evaluate, and Learn – equips project and program managers with the skills to guide AI systems through change and uncertainty. In this course, you’ll learn how to analyze performance data to plan retraining, evaluate algorithm families under real-world constraints, and design continuous-learning strategies with canary deployments and rollback safeguards. Through scenario-based discussions, hands-on activities, and practical tools like MLflow dashboards, evaluation matrices, and retraining calendars, you’ll practice making informed decisions under pressure. By the end, you’ll be able to detect risks early, balance accuracy and speed, and sustain reliable AI systems that align with business goals.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
The MCP Ecosystem in Mid 2026: Which Servers Are Actually Worth Adding to Your Stack
Learn how to choose the right servers for your MCP ecosystem from 13,000 options in mid 2026
Medium · Machine Learning
📰
Engineering Mindset — Stitching MLOps Together in One Modular Python Project
Learn to stitch MLOps tools together in a modular Python project for streamlined machine learning workflows
Medium · Machine Learning
📰
A Phased Blueprint for Migrating From Google Workspace to Microsoft 365
Learn a step-by-step approach to migrate from Google Workspace to Microsoft 365 with minimal downtime and zero data loss, understanding it as an infrastructure engineering challenge
Hackernoon
📰
Feature Freshness: The Forgotten Problem of MLOps
Learn how outdated features can cause production models to fail and why feature freshness is crucial in MLOps, to improve model performance and reliability
Medium · LLM
Up next
Pole Pruner How A Rope Lever Shears High Branches
Innoforge Studio
Watch →