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AI Engineering at Scale
Bridge the gap between AI prototypes and production systems. Learn the engineering practices, infrastructure patterns, and operational excellence required to run AI systems reliably at scale. Based on real-world experience building AI systems at top tech companies.
15 lessons8 hoursAdvanced
Course in Development
This course is currently being created. Follow me to get notified when it launches.
What You'll Learn
Model serving architectures and trade-offs
Latency optimization techniques
Caching strategies for AI systems
Monitoring and observability for ML
A/B testing AI features
Cost management at scale
Course Syllabus
1
The AI Engineering Mindset25 min
2
Model Serving Fundamentals40 min
3
Inference Optimization45 min
4
Batching and Queuing Strategies35 min
5
Caching for AI Systems30 min
6
GPU Infrastructure40 min
7
Monitoring and Alerting35 min
8
Drift Detection and Retraining30 min
9
A/B Testing AI Features35 min
10
Cost Optimization Strategies40 min
11
Security and Compliance30 min
12
Building AI Platforms45 min
13
Team Structure and Processes25 min
14
Future of AI Engineering20 min
15
Course Wrap-up and Resources15 min
Prerequisites
- •Experience with LLMs or ML models
- •Understanding of distributed systems
- •Production engineering experience
About the Author
PB
Pranay Bathini
Senior Software Engineer @ Booking.com
Leading development of critical infrastructure and mentoring team members in building scalable distributed systems.
Member of Booking Holdings India AI Committee. Designed and implemented guest verification backend for ID verification at scale.