Example
-
Updated
Mar 27, 2023 - Jupyter Notebook
Example
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.
Training deep learning models on AWS and GCP instances
MLOps for AWS SageMaker
Train machine learning models within a
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
A Spark library for Amazon SageMaker.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Serve machine learning models within a
Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Become a Certified Unicorn Developer and Participant in the API Token Economy
Amazon SageMaker Local Mode Examples
Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core
Toolkit for running PyTorch training scripts on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.
Library for automatic retraining and continual learning
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."