ML Model Development and Training on SageMaker
Description
Advanced model development techniques, training strategies, and optimization methods using SageMaker's comprehensive ML development platform.
Learning Objectives
Learners will master advanced model development and training techniques on SageMaker including custom algorithm development, distributed training, hyperparameter optimization, and model versioning. They will understand training job configuration, resource management, experiment tracking, and performance optimization strategies for scalable ML model development in production environments.
Topics (11)
Advanced custom algorithm development including container creation, script mode implementation, framework integration, and algorithm packaging for reusable deployment.
Comprehensive training job configuration including instance selection, storage optimization, network configuration, and training performance tuning.
Advanced distributed training including data parallelism, model parallelism, SageMaker distributed training library, and multi-GPU/multi-node training optimization.
Advanced hyperparameter optimization including AMT configuration, search strategies, early stopping, and multi-objective optimization for complex ML models.
Advanced model lifecycle management including SageMaker Experiments, model registry, version control, lineage tracking, and experiment comparison techniques.
Cost optimization strategies including managed spot training, checkpointing mechanisms, training job resumption, and resource allocation optimization.
Framework-specific training including TensorFlow estimators, PyTorch training scripts, MXNet integration, and framework version management in SageMaker.
Advanced transfer learning including model adaptation, fine-tuning strategies, domain adaptation, and integration of pre-trained models from model zoos.
Advanced model architectures including transformers, attention mechanisms, multi-modal fusion, and state-of-the-art architectures for various data types.
Comprehensive training best practices including convergence optimization, overfitting prevention, training stability, and systematic troubleshooting approaches.
Advanced debugging including SageMaker Debugger configuration, tensor analysis, training metrics monitoring, and performance bottleneck identification.