This course aims to teach learners how to continuously optimize microservices using machine learning techniques. The learning outcomes include understanding the challenges of performance tuning in a data center, applying Bayesian optimization for performance tuning, and building a continuous optimization service for microservices. The course covers topics such as performance stack, tuning, constraints, hidden variables, and implementation. The teaching method involves sharing experiences, pitfalls, and lessons from real projects, along with outlining a vision for optimization services. This course is intended for software engineers, developers, and professionals working with microservices and interested in leveraging machine learning for performance optimization.
Continuous Optimization of Microservices Using Machine Learning
Overview
Syllabus
Introduction
The Problem
Performance Stack
Performance Tuning
Performance Optimization
Performance Constraints
Hidden Variables
Performance Tuning Problem
Bayesian Optimization
Example
Gaussian Process
Expected Improvement
Bayesian Optimization as a Service
Bayesian Optimization API
Random Search
Twitter
Recap
Microservice
Staging
Setup
Results
Optimization changes
Takeaways
Implementation
Conclusion
Question
Taught by
Devoxx
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