Automate the model lifecycle: data, training and evaluation.
- 1
Data Pipelines for ML
Ingestion, preprocessing, feature engineering, versioning.
55 min - 2
Training Pipeline
Distributed training, GPU management, hyperparameter tuning.
1h - 3
Evaluation and Validation
Metrics, bias detection, slicing analysis, A/B testing.
50 min