Shipping Production-Grade AI Code

Shipping Production-Grade AI Code

This course is part of Python for AI Product Builders Specialization

Instructor: Vasco Patrício

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

You will learn to structure maintainable AI code with scripts, functions, classes, modules, and packages
You will learn to test, expose, and deploy AI applications using pytest, FastAPI, Pydantic, and containers or serverless platforms

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This course is part of the Python for AI Product Builders Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 2 modules in this course

A notebook that works is not an application that ships. Moving from exploration to production takes structure, tests, and a plan for deployment. In this course, you will learn how to structure, test, share, and deploy production-grade AI code.

You’ll learn how to move notebook logic into reusable scripts, organize code with functions, classes, modules, and packages, and keep dependencies flowing in one clear direction. You’ll explore how to test deterministic behavior and probabilistic model output with assertions, mocks, and structural checks, and how to expose AI features as validated, documented web APIs with FastAPI and Pydantic. The course also covers local, containerized, cloud, and serverless deployment, along with the configuration, secrets, logs, metrics, health checks, and alerts that keep deployed AI applications healthy. What makes this course unique is its focus on the engineering habits around the model. Not just making AI code work, but making it maintainable. By the end, you will be able to ship AI applications that are testable, shareable, and ready for production.

Structure maintainable AI code by moving notebook logic into scripts, choosing between functions and classes, and organizing modules and packages with clear dependency direction.

What's included

6 videos1 reading

Test AI applications with pytest, mocks, and structural checks, expose them as validated FastAPI web APIs, and compare deployment options with configuration and monitoring.

What's included

6 videos1 reading1 assignment

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Vasco Patrício

SkillsBooster Academy

7 Courses1,190 learners

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SkillsBooster Academy

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