autogluon / autogluon-cloudLinks
Autogluon-cloud aims to provide user tools to train, fine-tune and deploy AutoGluon backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management
☆23Updated 3 weeks ago
Alternatives and similar repositories for autogluon-cloud
Users that are interested in autogluon-cloud are comparing it to the libraries listed below
Sorting:
- A high performance data access library for machine learning tasks☆74Updated last year
- ☆58Updated last year
- Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors☆162Updated last year
- Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.☆413Updated last week
- Amazon SageMaker Managed Spot Training Examples☆51Updated last year
- Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.☆71Updated 5 months ago
- A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)☆252Updated last month
- CLI for building Docker images in SageMaker Studio using AWS CodeBuild.☆56Updated 3 years ago
- ☆73Updated last year
- Amazon SageMaker Local Mode Examples☆258Updated 4 months ago
- Unified storage framework for the entire machine learning lifecycle☆155Updated last year
- SageMaker Studio Docker CLI Extension☆13Updated last year
- This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use th…☆139Updated last week
- This repo will teach you how to deploy an ML-powered web app to AWS Fargate from start to finish using Streamlit and AWS CDK☆108Updated 4 years ago
- ☆135Updated last year
- Distributed XGBoost on Ray☆149Updated last year
- Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.☆407Updated last year
- Hosting code-server on Amazon SageMaker☆58Updated last year
- ☆87Updated last year
- Managing your machine learning lifecycle with MLflow and Amazon SageMaker☆153Updated 2 months ago
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆82Updated 3 years ago
- Introduction to Ray Core Design Patterns and APIs.☆71Updated last year
- Cloud provider cluster managers for Dask. Supports AWS, Google Cloud Azure and more...☆144Updated 3 weeks ago
- IbisML is a library for building scalable ML pipelines using Ibis.☆115Updated last month
- Library for automatic retraining and continual learning☆297Updated 11 months ago
- Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many de…☆74Updated last year
- Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.☆100Updated last year
- ☆96Updated 4 years ago
- Scaling Python Machine Learning☆49Updated last year
- Deploy production-grade Metaflow cloud infrastructure on AWS☆66Updated 4 months ago