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Duration 14 hours
Course Outline
Basics of Predictive Build Optimization
- Recognising bottlenecks in build systems
- Identifying sources of build performance data
- Mapping ML opportunities within CI/CD
Applying Machine Learning to Build Analysis
- Preparing build logs for data processing
- Extracting features from build-related metrics
- Choosing suitable ML models
Forecasting Build Failures
- Spotting critical failure indicators
- Training classification models
- Assessing the accuracy of predictions
Enhancing Build Speeds with ML
- Modelling patterns in build duration
- Estimating necessary resource levels
- Minimising variance to boost predictability
Smart Caching Strategies
- Identifying build artifacts suitable for reuse
- Developing ML-driven cache policies
- Overseeing cache invalidation
Embedding ML into CI/CD Pipelines
- Integrating prediction steps into build workflows
- Safeguarding reproducibility and traceability
- Operationalising models for ongoing improvement
Monitoring and Continuous Feedback Loops
- Gathering build telemetry
- Streamlining performance review cycles
- Retraining models using new data
Scaling Predictive Build Optimization
- Oversight of large-scale build ecosystems
- Utilising ML for resource forecasting
- Integration with multi-cloud build platforms
Recap and Future Directions
Requirements
- A foundational understanding of software build pipelines
- Practical experience with CI/CD tools
- Basic familiarity with machine learning principles
Target Audience
- Build and release engineers
- DevOps practitioners
- Platform engineering teams
