ML Engineering
AutoML — Automating the Machine Learning Pipeline
Learn how AutoML systems automate the end-to-end machine learning pipeline, from data preprocessing to model selection and hyperparameter tuning.
- Neural Architecture Search — Automatically discovering optimal neural network designs
- Hyperparameter Optimization — Efficiently searching the hyperparameter space
- Feature Engineering — Automated feature creation and selection
"Automate the tedious, focus on the creative."
AutoML — Automated Machine Learning
AutoML automates the ML pipeline — from data preprocessing to model deployment.
AutoML Pipeline Architecture
Hyperparameter Optimization
Neural Architecture Search (NAS)
Multi-Fidelity Optimization
Key Takeaways
What to Learn Next
-> Model Selection and Hyperparameter Tuning Complete Guide Learn about model selection and hyperparameter tuning complete guide.
-> Feature Engineering — Complete Guide Learn about feature engineering — complete guide.
-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.
-> MLOps — Machine Learning Operations Complete Guide Learn about mlops — machine learning operations complete guide.
-> Ensemble Methods — Bagging, Boosting, Stacking Complete Guide Learn about ensemble methods — bagging, boosting, stacking complete guide.
-> ML System Design — Architecture and Production Patterns Learn about ml system design — architecture and production patterns.