Machine Learning Engineering Done Right
summary
Ten concrete recommendations cover design, coding and ops: use SageMaker and SageMaker Clarify for bias detection and SHAP explainability, prefer frameworks over bespoke stacks, and enforce a 20-line rule plus testable code and avoid broad try/catch. Deploy with CI/CD into Lambda, Fargate or SageMaker endpoints, run automated HPO with 15 parallel experiments to find best hyperparameters, use transient p2.xlarge (Hugging Face / BERT) instances to cut cost, and run 80/20 production variants for A/B model rollout.
AI-written summary · about
chapters (12)
- 0:00Introduction to machine learning engineering
- 3:41Business and customer needs
- 5:04Production-level Python code
- 6:49Python coding guidelines
- 10:19Python design patterns
- 12:03Continuous integration and deployment pipelines
- 13:45ML frameworks and platforms
- 20:00Model drift and bias detection
- 24:57Hyperparameter optimization
- 29:48Cost optimization with ML instances
- 30:31Securing ML environments
- 32:38Closing remarks & Q&A
related talks
43mDesigning and Building Complex Machine Learning Projects
34mLat Designing and Building Complex Machine Learning....
37mStreamlining Machine Learning Operations
37mStreamlining Machine Learning Operations
44mPragmatic Machine Learning in the Cloud
25mDesigning an AWS Well Architected Framework for AI/ML
33mBuild, train, and deploy ML models using Amazon SageMaker
39mApplying deployment oriented mindset for building Machine Learning models