In this bootcamp, students will learn how to fine-tune Transformer vision models with Hawking Face and SageMaker, deploy them to real-time endpoints, and monitor model behavior with CloudWatch. They will also learn how to use tools like Lambda, API Gateway, S3, and Bedrock to collect low-confidence predictions, label new examples, and feed them back into an automated retraining cycle. Finally, they will learn how to create evaluation and deployment gates to ensure that the new model performs better before replacing the old one.

What you will learn

• Understanding the full MLOps lifecycle from training to retraining

• Fine-tuning Transformer vision models using Face Hacking on Amazon Web Services

• Preparing S3 datasets for cloud training of models

• Deploying Amazon Web Services real-time endpoints for live predictions

• Monitoring model confidence and activity using CloudWatch

• Building Amazon Web Services Pipelines for Automated Machine Learning Workflows

• Connecting Lambda, API Gateway, S3, and BDR services together

• Automatic retraining of models and determining conditions for deployment in the operational environment

This course is suitable for people who:

• They are looking to master key skills to enter the advanced artificial intelligence and machine learning job market.

• They want to go beyond model building and learn how to manage infrastructure and production operations.

• They work with Amazon Web Services and aim to build scalable AI systems.

• They want to implement automated workflows to monitor, evaluate, and retrain AI models.

Installation Guide

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Subtitles: None

Quality: 1080p