ACK service controller for Amazon SageMaker
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Updated
Aug 7, 2024 - Python
ACK service controller for Amazon SageMaker
A library for training and deploying machine learning models on Amazon SageMaker
Like PyTorch for ML infra. Iterable, debuggable, multi-cloud, 100% reproducible across research and production.
Probabilistic time series modeling in Python
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Amazon SageMaker Local Mode Examples
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Workshop CDK Template to provision infra for the Deep Visual Search workshop
Library for automatic retraining and continual learning
Deploy your AI/ML model to Amazon SageMaker for Real-Time Inference and Batch Transform using your own Docker container image.
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.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
A lambda function split preprocessed data into training and validation used for starting a training job within AWS SageMaker.
Adapts algorithms that implement the Grand Challenge inference API for running in SageMaker
An Exasol extension to interact with AWS SageMaker from inside the database
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
End-to-end AWS Pipelines: Preprocessing till Monitoring
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."