Example
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Updated
Aug 1, 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
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
MLOps for AWS SageMaker
Train machine learning models within a
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
Serve machine learning models within a
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
A Spark library for 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
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Amazon SageMaker Local Mode Examples
Become a Certified Unicorn Developer and Participant in the API Token Economy
Toolkit for running PyTorch training scripts on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.
An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.
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
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