What background do I need for this MLOps course?

You should have experience with Python and machine learning fundamentals. Familiarity with training ML models is helpful, but you don't need prior experience deploying models to production or working with MLOps tools.

What is MLOps and why does it matter?

MLOps (machine learning operations) is the practice of deploying and maintaining AI models in production. It matters because models that work on your laptop often fail with real users—MLOps helps you build systems that stay accurate, secure, and cost-effective over time.

What jobs use machine learning operations skills?

MLOps skills are essential for machine learning engineers, AI engineers, data scientists moving into production roles, and DevOps engineers working with AI systems. These roles exist across tech, finance, healthcare, and any industry deploying AI.

Will this course cover A/B testing for AI models?

Yes, you'll learn both offline and online evaluation methods. The course covers A/B testing to measure real-world model performance and make data-driven decisions about deploying model updates.

How does this course address AI safety concerns?

You'll learn to detect and prevent three critical threats: instruction manipulation (users overriding system behavior), information leakage (exposing private data), and resource abuse. The course also covers bias detection and techniques for ensuring fair performance across user groups.

What is model drift and how do I detect it?

Model drift occurs when real-world conditions change from your training data, causing performance to decline. This course teaches you to monitor for data drift (changing input patterns) and performance drift (declining accuracy), plus automated detection strategies using statistical tests.

Course

Best Practices in AI Deployment

Learn machine learning operations best practices to deploy, monitor, and maintain production AI systems that are reliable, secure, and cost-effective.

This course includes

Skill level
Intermediate
Time to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary

1 hour
Projects
1
Prerequisites
2 courses

We suggest you complete the following courses before you get started with Best Practices in AI Deployment:
Learn Python 3
Learn Intermediate Python 3

About this course

Production AI is more than training models—it’s about keeping them running reliably for real users. This MLOps course teaches you essential machine learning operations practices through building an AI-powered customer support chatbot. You’ll learn to monitor performance metrics, protect user privacy, detect model drift, optimize costs, and respond to incidents. Master the skills to deploy AI systems that stay accurate, secure, and efficient in the real world. Perfect for developers ready to take their models from laptop to production.

Skills you'll gain

Monitor ML model performance metrics
Implement data privacy protections
Detect and respond to model drift
Optimize AI system costs

Syllabus

Best Practices in AI Deployment
Master production AI essentials including performance monitoring, privacy protection, model evaluation, cost optimization, bias detection, and incident response

lesson Best Practices in AI Deployment
project Analyzing Recommendation System Performance Across Model Versions
quiz Best Practices in AI Deployment Quiz
informational Best Practices in AI Deployment Next Steps
Certificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.

The platform

Hands-on learning

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Projects in this course

practice Project
Analyzing Recommendation System Performance Across Model Versions
Analyze the performance and cost-effectiveness of an e-commerce recommendation system across three model versions. You'll investigate whether newer models deliver better results, identify performance differences across customer segments, and determine which model version provides the best ROI.

Earn a certificate of completion

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Reviews from learners

The progress I have made since starting to use codecademy is immense! I can study for short periods or long periods at my own convenience - mostly late in the evenings.
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I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.
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Codecademy Learner @ UK
Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.
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Codecademy Learner @ USA

Our learners work at

Frequently asked questions about Best Practices in AI Deployment

You should have experience with Python and machine learning fundamentals. Familiarity with training ML models is helpful, but you don’t need prior experience deploying models to production or working with MLOps tools.

Join over 50 million learners and start Best Practices in AI Deployment today!

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Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.
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Best Practices in AI Deployment

About this course

Skills you'll gain

Syllabus