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Designing ML Systems - Model Development and Offline Evaluation
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Model Development and Offline Evaluation
Designing ML Systems - Model Development and Offline Evaluation
ML Algorithms
Model Development & Training
Evaluating ML Models
Experiment tracking and versioning
Debugging ML Models
Distributed Training
state-of-the-art
ML models fail silently
Data parallelism
Model parallelism
Pipeline parallelism
Model Offline Evaluation
Random baseline
Simple heuristic
Zero rule baseline
Human baseline
Perturbation tests
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Designing ML Systems - Model Development and Offline Evaluation
Model development is an iterative process. After each iteration, you’ll want to compare your model’s performance against its performance in previous iterations and evaluate how suitable this iteration is for production. To build an ML model, we first need to select the ML model to build. There are so many ML algorithms out there, with more actively being developed. In this video, we will discusses different aspects of model development, such as debugging, experiment tracking and versioning, and distributed training.
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