Probabilistic time series modeling in Python
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Updated
May 24, 2024 - Python
Probabilistic time series modeling in Python
A library for training and deploying machine learning models on Amazon SageMaker
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
Training deep learning models on AWS and GCP instances
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
LLMs and Machine Learning done easily
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Serve machine learning models within a 🐳 Docker container 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.
Library for automatic retraining and continual learning
Amazon SageMaker Local Mode Examples
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.
Joining the modern data stack with the modern ML stack
Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors
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)
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.
Build and deploy a serverless data pipeline on AWS with no effort.
Add a description, image, and links to the sagemaker topic page so that developers can more easily learn about it.
To associate your repository with the sagemaker topic, visit your repo's landing page and select "manage topics."