aws-samples/aws-lambda-docker-serverless-inference
Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
This project helps machine learning engineers or MLOps specialists deploy trained machine learning models for prediction without managing servers. You provide a trained model from frameworks like scikit-learn, XGBoost, TensorFlow, or PyTorch, and it outputs a scalable, pay-per-use inference endpoint. This is ideal for those who need to serve predictions from various data types, including text, images, or tabular data.
100 stars. No commits in the last 6 months.
Use this if you need to serve predictions from your machine learning models (e.g., for object detection, sentiment analysis, or classification) and want to minimize infrastructure management and cost, especially for infrequent or bursty usage.
Not ideal if your application requires extremely low latency for every single inference request, as serverless functions can sometimes introduce a slight delay.
Stars
100
Forks
19
Language
Jupyter Notebook
License
MIT-0
Category
Last pushed
Jul 25, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aws-samples/aws-lambda-docker-serverless-inference"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning...
aws/sagemaker-xgboost-container
This is the Docker container based on open source framework XGBoost...
aws-samples/aws-ml-enablement-workshop
組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ
aws/sagemaker-training-toolkit
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.