AI Engineering
AI Engineering
Learn to build production AI systems and end-to-end machine learning training and inference workflows.
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Course overview
Learn to build production AI systems and end-to-end machine learning training and inference workflows.
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What you will learn
AI Engineering (a.k.a applied ML) focuses on deploying AI systems in production and building E2E workflows that modularizes the ML training and inference lifecycles. According to the 2015 NIPS paper “Hidden Technical Debt in Machine Learning Systems” by Sculley et al., a mature ML system may consist of only 5% ML code, with the remaining 95% being “glue code” that integrates various components. This course aims to explore the different aspects of this “glue code” and introduce participants to widely adopted practices for building and maintaining inference and training pipelines for their businesses. The course will alternate between lecture slides and hands-on demonstrations, with one week dedicated to slides content and the next to practical application.
The published course outline alternates lecture and lab weeks, covering AI engineering and ML system design, data pipelines and engineering, training data and reducing the ML footprint, evaluating ML systems, ML deployment and business SLAs, and monitoring and retraining strategies.
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Lecture syllabus
Video titles and durations as published on Arabsera. The advertised lecture total may differ from the number of video entries, which can include introductions, tutorials, and split sessions.
Video titles and durations
- Free Introductory Lecture Course Introduction
- ML Crash Course for AI Engineers Introduction
- ML Crash Course for AI Engineers ML Introduction and KNN
- ML Crash Course for AI Engineers Linear Classification
- ML Crash Course for AI Engineers Perceptron Learning
- ML Crash Course for AI Engineers Regression Analysis
- ML Crash Course for AI Engineers Evaluation of ML Models
- ML Crash Course for AI Engineers Overfitting and Mitigation Strategies
- Intro to AI Engineering & ML System Design Slides Week Recording
- Intro to AI Engineering & ML System Design Lab Week Recording
- Data Pipelines and Engineering Slides Week Recording
- Data Pipelines and Engineering Lab Week Recording
- Training Data & Reducing ML Footprint Slides Week Recording
- Training Data & Reducing ML Footprint Lab Week Recording
- Developing & Evaluating ML Systems Slides Week Recording
- Developing & Evaluating ML Systems Lab Week Recording
- ML Deployment & Business SLAs Slides Week Recording
- ML Deployment & Business SLAs Lab Week Recording
- ML E2E Pipeline Lab Week Recording
- Monitoring & Retraining Strategies Slides Week Recording
- Monitoring & Retraining Strategies Lab Week Recording
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Teaching support (check your plan)
- Discussion groups
- TA-GPT (coming soon)
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Textbook
No textbook specified.
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Certificate
Awarded upon completion of all course activities.