📄 Classify and recognize documents efficiently with this production-ready MLOps platform using leading machine learning and orchestration tools.
🚀 Build an end-to-end MLOps pipeline for credit risk assessment using AWS, integrating data processing, machine learning, and secure infrastructure.
🩻 Detect lung nodules in CT scans using YOLOv8 and AWS SageMaker for early lung cancer diagnosis and efficient model deployment.
🔍 Monitor cloud environments with AI-driven insights, anomaly detection, and automated incident responses for better operational efficiency.
📊 Build a reliable real-time data pipeline for Windows using PySpark, ensuring quality data flow from raw ingestion to curated datasets.
This repo shows how foundational model hallucinates and how we can fix such hallucinations using fine-tuning them
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
A library for training and deploying machine learning models on Amazon SageMaker
Real academic research projects for cloud computing. Pre-built workflows across 20+ domains. Launch in minutes, scale when ready.
End-to-end data lake architecture with ETL pipelines (Glue), SQL analytics (Athena), ML fraud detection (SageMaker), and generative AI insights (Bedrock). Infrastructure as Code with Terraform.
Know How Guide and Hands on Guide for AWS
Application implementation with business use cases for safely utilizing generative AI in business operations
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.
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