Probabilistic time series modeling in Python
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Updated
Aug 6, 2024 - Python
Probabilistic time series modeling in Python
A library for training and deploying machine learning models on Amazon SageMaker
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Dispatch and distribute your ML training to "serverless" clusters in Python, like PyTorch for ML infra. Iterable, debuggable, multi-cloud/on-prem, identical across research and production.
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
Training deep learning models on AWS and GCP instances
LLMs and Machine Learning done easily
Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Library for automatic retraining and continual learning
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 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
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.
3D Dense Connected Convolutional Network (3D-DenseNet for action recognition)
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."