Meet the Authors
Chris Fregly
Chris Fregly is a Principal Solution Architect for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS."
Chris is also the Founder of the global meetup series titled, "Data Science on AWS." He regularly speaks at AI and Machine Learning conferences across the world including O'Reilly AI, Open Data Science Conference (ODSC), Big Data London, Big Data Spain, and Nvidia GPU Technology Conference (GTC).
Antje Barth
Antje Barth is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. She is co-author of the O'Reilly Book, "Data Science on AWS."
Antje is also co-founder of the global "Data Science on AWS" Meetup. She frequently speaks at AI and Machine Learning conferences and meetups around the world, including the O'Reilly AI and Strata conferences. Besides ML/AI, Antje is passionate about helping developers leverage Big Data, container and Kubernetes platforms in the context of AI and Machine Learning.
Shelbee Eigenbrode
Shelbee Eigenbrode is a Principal Solution Architect for Generative AI at Amazon Web Services (AWS) based in Denver, Colorado. She is co-author of the Packt Book, "Data Science on AWS."
Shelbee is also co-founder of the Denver AWS AI/ML Meetup. She frequently speaks at AI and Machine Learning conferences and meetups around the world. Besides Generative AI, Shelbee is passionate about MLOps, LLMOps, and statistics.
Popular Talks
Reduce LLM Toxicity with PEFT and RLHF
Learn how to combine parameter efficient fine tuning (PEFT) with reinforcement learning from human feedback (RLHF) for lightweight detoxification of an LLM on a single GPU.
Fine-tuning LLMs with Amazon SageMaker
Build an end-to-end pipeline to fine-tune and deploy a generative large-language model using Amazon SageMaker, including SFT, PEFT, and RLHF workflows.
Scaling AI with Ray on AWS and Kubernetes
Deep dive into distributed Ray for high-performance ML workloads, with tips for optimizing on AWS and a demo using Ray's AI Runtime to train BERT models on EKS.
End-to-End ML Pipeline with BERT and TensorFlow
Build a complete AI/ML pipeline for NLP with SageMaker: data ingestion, feature engineering, model training, hyperparameter tuning, and deployment.
Quantum Machine Learning Fundamentals
Demystifying quantum computing and quantum machine learning. Learn about quantum algorithms and how hybrid quantum-classical approaches solve today's problems.
ML Workflows with Kubeflow Pipelines
Get started with Kubeflow Pipelines on AWS and integrate SageMaker features for data labeling, distributed training, and scalable model deployment.
Amazon SageMaker AutoPilot
Discover how AutoPilot automatically inspects data, picks algorithms, trains models, and provides complete visibility into the model creation process.
Human-in-the-Loop with Augmented AI
Demonstrate human-in-the-loop workflows using Amazon A2I, where AI handles routine predictions and humans focus on complex edge cases.
Cost Optimization on Amazon SageMaker
Control and optimize your SageMaker costs across notebooks, training jobs, hyperparameter tuning, batch predictions, and GPU usage.
Model Monitoring with SageMaker
Deploy models to production and monitor performance degradation in real-time using SageMaker Model Monitor and automated alerting.
NLP with Amazon Comprehend and Kendra
Analyze documents and derive insights from text data sources using Comprehend for NLP and Kendra for enterprise search.
Data Lake House Architecture on AWS
Query data across your data warehouse, data lake, and operational databases using Amazon Redshift lake house architecture.