Video: Organize, Track, and Evaluate ML Training Runs With Amazon SageMaker Experiments

Curso: Amazon SageMaker Technical Deep Dive Series
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Básico

Categorias: DevOps,
Descrição:
Amazon SageMaker Technical Deep Dive Series

Progresso:

Fully-Managed Notebook Instances with Amazon SageMaker - a Deep Dive
Built-in Machine Learning Algorithms with Amazon SageMaker - a Deep Dive
Bring Your Own Custom ML Models with Amazon SageMaker
Train Your ML Models Accurately with Amazon SageMaker
Deploy Your ML Models to Production at Scale with Amazon SageMaker
Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning
Scale up Training of Your ML Models with Distributed Training on Amazon SageMaker
Use the Deep Learning Framework of Your Choice with Amazon SageMaker
Learn to Analyze the Co-Relation in Your Datasets Using Feature Engineering with Amazon SageMaker
Get Scheduled Predictions on Your ML Models with Amazon SageMaker Batch Transform
Build Highly Accurate Training Datasets at Reduced Costs with Amazon SageMaker Ground Truth
Organize, Track, and Evaluate ML Training Runs With Amazon SageMaker Experiments
Automatically Build, Train, and Tune ML Models With Amazon SageMaker Autopilot
Deploy Multiple ML Models on a Single Endpoint Using Multi-model Endpoints on Amazon SageMaker
Amazon SageMaker Studio - A Fully Integrated Development Environment For Machine Learning
Analyze, Detect, and Get Alerted on Problems With Training Runs Using Amazon SageMaker Debugger
Centrally track and manage your model versions in Amazon SageMaker | Amazon Web Services
Amazon SageMaker MLOps | Amazon Web Services
Build ML models at scale with Amazon SageMaker Studio Notebooks | Amazon Web Services

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