From the course: Machine Learning with SageMaker by Pearson
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Machine learning with SageMaker summary - Amazon SageMaker Tutorial
From the course: Machine Learning with SageMaker by Pearson
Machine learning with SageMaker summary
Congratulations on completing the AWS Certified Machine Learning Associate Exam Preparation course. We've covered everything from preparing and managing data, developing and deploying machine learning models, and monitoring, optimizing and securing your solutions in AWS, key focus areas and practical tips to approach the certification with confidence. Now it's time to put your knowledge into action take the next step toward earning your certification. Remember, practice and hands-on experience are key to success. Best of luck on your exam and thank you for letting me be a part of your journey.
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Contents
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Module introduction30s
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Learning objectives32s
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Overview of SageMaker built-in algorithms and JumpStart models8m 13s
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SageMaker algorithms demonstration25m 3s
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Setting up and running SageMaker training jobs6m 14s
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SageMaker training demonstration21m 48s
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Hyperparameter tuning with SageMaker automatic model tuning7m 42s
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Hyperparameter tuning demonstration21m 48s
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Preventing overfitting and underfitting8m 3s
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Model over/underfitting demonstration13m 39s
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Learning objectives39s
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Model evaluation metrics: Accuracy, precision, and recall9m 39s
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Using SageMaker Clarify for bias detection and interpretability7m 40s
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Comparing model performance using A/B testing5m 47s
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Model A/B testing demonstration6m 26s
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Managing model versions with SageMaker Model Registry5m 55s
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Model registry demonstration10m 48s
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Module introduction35s
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Learning objectives34s
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Real-time inference with SageMaker endpoints9m 58s
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Real-time inference demonstration8m 51s
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Batch inference and asynchronous inference6m 29s
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Batch and asynchronous inference demonstration14m 50s
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Using SageMaker Neo for edge deployment7m 32s
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SageMaker edge deployment demonstration12m 13s
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Learning objectives31s
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Building and automating ML pipelines in SageMaker6m 40s
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SageMaker pipeline demonstration34m 3s
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Integrating data processing and training steps6m 56s
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Training and data processing in SageMaker pipelines demonstration10m 17s
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Triggering pipelines with EventBridge for retraining7m 15s
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Triggering SageMaker pipelines via EventBridge demonstration9m 51s
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Module introduction34s
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Learning objectives33s
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Using SageMaker Model Monitor for data drift and quality5m 49s
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SageMaker Model Monitor demonstration6m 30s
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Setting up alerts and CloudWatch dashboards7m 41s
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Cost optimization with auto-scaling and SageMaker Savings Plans7m 9s
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SageMaker auto scaling demonstration9m 31s
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