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Part 1: Before Pretraining
1 An Introduction to Pretraining Foundation Models
2 Dataset Preparation: Part One
3 Model Preparation
Part 2: Configure Your Environment
4 Containers and Accelerators on the Cloud
5 Distribution Fundamentals
6 Dataset Preparation: Part Two, the Data Loader
Part 3: Train Your Model
7 Finding tne Right Hyperparameters
8 Large-Scale Training on SageMaker
9 Advanced Training Concepts
Part 4: Evaluate Your Model
10 Fine-Tuning and Evaluating
11 Detecting, Mitigating, and Monitoring Bias
12 How to Depoloy Your Model
Part 5: Deploy Your Model
13 Prompt Engineering
14 MLOps for Vision and Language
15 Future Trends in Pretraining Foundation Models
Index
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Webber, Emily
Emily Webber is a Principal Machine Learning Specialist Solutions Architect at Amazon Web Services. She has assisted hundreds of customers on their journey to ML in the cloud, specializing in distributed training for large language and vision models. She mentors Machine Learning Solution Architects, authors countless feature designs for SageMaker and AWS, and guides the Amazon SageMaker product and engineering teams on best practices in regards around machine learning and customers. Emily is widely known in the AWS community for a 16-video YouTube series featuring SageMaker with 160,000 views, plus a Keynote at O'Reilly AI London 2019 on a novel reinforcement learning approach she developed for public policy.
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