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Duration 14 hours
Course Outline
Foundations of Predictive Build Optimization
- Understanding bottlenecks within build systems.
- Identifying sources of build performance data.
- Mapping ML opportunities within CI/CD pipelines.
Machine Learning for Build Analysis
- Preprocessing data from build logs.
- Extracting features from build-related metrics.
- Selecting appropriate ML models.
Predicting Build Failures
- Identifying key indicators of failure.
- Training classification models.
- Evaluating the accuracy of predictions.
Optimizing Build Times with ML
- Modelling patterns in build duration.
- Estimating resource requirements.
- Reducing variance to improve predictability.
Intelligent Caching Strategies
- Detecting reusable build artifacts.
- Designing cache policies driven by ML.
- Managing cache invalidation.
Integrating ML into CI/CD Pipelines
- Embedding prediction steps into build workflows.
- Ensuring reproducibility and traceability.
- Operationalizing models for continuous improvement.
Monitoring and Continuous Feedback
- Collecting telemetry from builds.
- Automating performance review cycles.
- Retraining models based on new data.
Scaling Predictive Build Optimization
- Managing large-scale build ecosystems.
- Forecasting resources using ML.
- Integrating with multi-cloud build platforms.
Summary and Next Steps
Requirements
- A solid understanding of software build pipelines.
- Hands-on experience with CI/CD tools.
- Familiarity with fundamental machine learning concepts.
Target Audience
- Build and release engineers.
- DevOps practitioners.
- Platform engineering teams.
