Amazon Bedrock Tutorial: Single and Batch LLM Inference on AWS

About this lesson

In this video, we walk through how to use Amazon Bedrock for LLM inference without managing any infrastructure. You'll learn both single-request inference (one prompt at a time) and batch inference (processing hundreds of prompts efficiently from S3). You'll learn how to: - Understand regional limitations and why we focus on inference, not fine-tuning - Invoke Bedrock models with a simple API call for single predictions - Format and upload datasets to S3 for batch processing - Create and monitor batch inference jobs in Bedrock - Retrieve batch results from S3 and evaluate with ROUGE scores - Establish baselines quickly across multiple foundation models Timestamps: 0:00 - Introduction to Bedrock inference 0:39 - Regional limitations and fine-tuning constraints 2:49 - Single inference API overview 4:42 - Single inference example walkthrough 5:26 - Batch inference for large-scale processing 7:26 - Creating batch inference jobs 8:47 - Batch job completion and results 9:22 - Evaluating predictions with ROUGE scores Watch this video if you're building LLM applications on AWS, need to establish baselines across multiple models, or want to understand when managed inference makes sense versus self-hosted deployment. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI/ML professionals build and evaluate intelligent, goal-driven systems and showcase them through certifications, competitions, and real-world project publications. Learn more: https://www.readytensor.ai/ Like the video? Subscribe and let us know what other AWS deployment techniques you want us to cover!

Original Description