There are 16 repositories under sagemaker topic.
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
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
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Like PyTorch for ML infra. Iterable, debuggable, multi-cloud, 100% reproducible across research and production.
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
すぐに業務活用できるビジネスユースケース集付きの安全な生成AIアプリ実装
Training deep learning models on AWS and GCP instances
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
A Spark library for Amazon SageMaker.
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
Amazon SageMaker Local Mode Examples
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
Toolkit for running PyTorch training scripts on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.
Become a Certified Unicorn Developer and Participant in the API Token Economy
Joining the modern data stack with the modern ML stack
AWS Data/MLServices sample code & notes for my LinkedIn Learning courses
Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core
Generating Cat images using StyleGAN
Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors
Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.
SageMakerで機械学習モデルを構築、学習、デプロイする方法が学べるNotebookと教材集
A curated list of references for Amazon SageMaker
Amazon SageMaker operator for Kubernetes
Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
3D Dense Connected Convolutional Network (3D-DenseNet for action recognition)