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Yapay ZekâÖnerilenAIML-SYS-020

Retraining and Continual Learning in Production

When and how to update deployed models as data drifts, without forgetting or breaking downstream consumers.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
3 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can choose a retraining trigger and schedule for a drifting system and name the risk of each option.

Bu konuya giden rota

Rota AIML-SYS-020
What Is a Model? Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Datasets and Splits Classification Framing Linear Regression I: Prediction and Residuals Embeddings Sampling and Splits Scores and Logits Loss and Fitting: Squared Error Distribution Shift The Artificial Neuron Doğrusal Regresyon II: En Küçük Kareler Uydurma Objective Functions and Empirical Risk Overfitting and Underfitting Activation Functions Gradient Descent in Machine Learning Training vs Serving The Multilayer Perceptron Monitoring and Drift Stochastic and Mini-batch Optimization Transfer Learning Fine-Tuning Catastrophic Forgetting Retraining and Continual Learning in Production