Build/Train/Deploy Machine Learning Models
Prepare your Datasets at Scale using Apache Spark and SageMaker Data Wrangler [Level 300]
Discover ways to use Apache Spark on AWS to analyze large datasets, perform data quality checks, transform raw data into machine learning features, and train predictive models.
Orchestrate and Automate Machine Learning Workflows with SageMaker Pipelines [Level 300]
In this session, you'll see how to create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
Comparing Models in Production with Multi-Armed Bandits and Reinforcement Learning [Level 300]
Using the popular Hugging Face Transformers open source library for BERT to train and deploy multiple natural language understanding (NLU) models.
Reduce Training Time and Cost with SageMaker Debugger [Level 300]
In this session, we walk through how to use the real-time training metrics and set up alerts so you can reduce troubleshooting time, training costs and improve model quality.
Standardize and Automate your Feature Engineering Workflows with SageMaker Feature Store [Level 300]
Learn how to solve all these problems with Amazon SageMaker Feature Store, and how to use it with both the SageMaker Studio user interface and the SageMaker SDK.
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