#Sagemaker
Showing 45 of 45 repositories tagged #sagemaker, ranked by stars
Example π Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using π§ Amazon SageMaker.
Probabilistic time series modeling in Python
A library for training and deploying machine learning models on Amazon SageMaker
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS
Application implementation with business use cases for safely utilizing generative AI in business operations
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
Train machine learning models within a π³ Docker container using π§ Amazon SageMaker.
Training deep learning models on AWS and GCP instances
LLMs and Machine Learning done easily
Serve machine learning models within a π³ Docker container using π§ Amazon SageMaker.
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
Library for automatic retraining and continual learning
Scan 30+ AWS services. Find cost waste. Detect security gaps. Map your attack surface. One command.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.
Become a Certified Unicorn Developer and Participant in the API Token Economy
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
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A set of Docker images that include popular frameworks for machine learning, data science and visualization.
This repository includes code and demos for session "Unlock insights with AWS GenAI services" (re:Invent BOA303)
Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
SyntheticSun is a defense-in-depth security automation and monitoring framework which utilizes threat intelligence, machine learning, managed AWS security services and, serverless technologies to continuously prevent, detect and respond to threats.
Deploy open-source LLMs on AWS in minutes β with OpenAI-compatible APIs and a powerful CLI/SDK toolkit.
Projects developed by Domino's R&D team
Demonstration application showing how Neo4j works with Amazon Bedrock
Detect Defects in Products from their Images using Amazon SageMaker
Deploy your AI/ML model to Amazon SageMaker for Real-Time Inference and Batch Transform using your own Docker container image.
Hands on lab for Neo4j and Amazon Bedrock
Sagemaker pipeline for AWS Summit New York
Hosting code-server on Amazon SageMaker
Generative AI on AWS Immersion Day
Secure and scalable MLOps platform on AWS using Terraform.
This repository features three demos that can be effortlessly integrated into your AWS environment. They serve as a practical guide to leveraging AWS services for crafting a sophisticated Large Language Model (LLM) Generative AI, geared towards creating a responsive Question and Answer Bot and localizing content generation.
DAIVI is a reference solution with IAC modules to accelerate development of Data, Analytics, AI and Visualization applications on AWS using the next generation Amazon SageMaker Unified Studio. The goal of the DAIVI solution is to provide engineers with sample infrastructure-as-code modules and application modules to build their data platforms.
2019 AWS Certified Machine Learning β Study Notes
Building an AWS Solution Architect Agent with Generative AI
META LLAMA3 GENAI Real World UseCases End To End Implementation Guide
This project uses the open-source model Mistral Small, deployed in Amazon SageMaker or invoked via API on Amazon Bedrock, to enable users to chat with their database using natural language, without writing any code or SQL query.
This project shows the demos about how to generate stable diffusion images using AWS SageMaker Jumpstat.
SageMaker Ployglot based RAG opensearch
Deploy Audiocraft Musicgen on Amazon SageMaker using SageMaker Endpoints for Async Inference.
This repository contains samples for fine-tuning embedding models using Amazon SageMaker. Embedding models are useful for tasks such as semantic similarity, text clustering, and information retrieval. Fine-tuning these models on your specific domain data can greatly improve their performance.
An Amazon SageMaker Container for Hugging Face Inference on Graviton and Intel CPUs