Day176 - MLOps Review: Data Distribution Shifts and Monitoring (3)
Designing Machine Learning Systems: Data Distribution Shifts (2) (Detecting & Addressing Data Distribution Shifts)
Designing Machine Learning Systems: Data Distribution Shifts (2) (Detecting & Addressing Data Distribution Shifts)
Designing Machine Learning Systems: Causes of ML System Failures (2) (Correcting Degenerate Feedback Loops) & Data Distribution Shifts (1) (Covariate, Co...
Designing Machine Learning Systems: Causes of ML System Failures (1) (Production data differing from training data, Edge Cases, and Degenerate Feedback Loops)
Designing Machine Learning Systems: ML on the Cloud and on the Edge & Model Optimization (AutoTVM & WebAssembly)
Designing Machine Learning Systems: Model Comparison (Low-Rank Factorization, Knowledge Distillation, Pruning, & Quantization)
Designing Machine Learning Systems: Model Deployment and Batch Prediction Versus Online Prediction (Unifying Batch and Streaming Pipeline)
Designing Machine Learning Systems: Model Offline Evaluation (Methods: Perturbation Tests, Invariance Tests, etc.)
Designing Machine Learning Systems: Auto ML (Hyperparameter Tuning & NAS), Model Offline Evaluation (1) (Establishing Baselines)
Designing Machine Learning Systems: Experiment Tracking, Versioning, and Distributed Training (Data Parallel)
Designing Machine Learning Systems: Model Selection, Evaluating ML Models, & Ensemble Method (Bagging, Boosting, and Stacking)