amazon-sagemaker

(Unofficial) curated list of awesome workshops found around in the internet. As we all have been there, finding that workshop that you have just attended shouldn't be hard. The idea is to provide an easy central repository, in a collaborative way.

A Spark library for Amazon SageMaker.

Safe blue/green deployment of Amazon SageMaker endpoints using AWS CodePipeline, CodeBuild and CodeDeploy.

Open innovation with 60 minute cloud experiments on AWS

Amazon SageMaker Local Mode Examples

Docker images that replicate the Amazon SageMaker Notebook instance.

Running your TensorFlow models in Amazon SageMaker

MLOps workshop with Amazon SageMaker

End to end Machine Learning with Amazon SageMaker

Hands-on demonstrations for data scientists exploring Amazon SageMaker

Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large corpus of protein sequences.

Amazon SageMaker Managed Spot Training Examples

This solution combines Amazon Pinpoint with Amazon SageMaker to help automate the process of collecting customer data, predicting customer churn using ML, and maintaining a tailored audience segment for messaging.

Implementation of Image Classification using Visual Transformers in Amazon SageMaker based on the ideas from research paper - Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

This repo contains various use-cases of deep-learning implemented in Pytorch. It also contains summarized notes of each chapter from the book, 'Deep Learning' written by Ian Goodfellow.

Snowflake Guide: Building a Recommendation Engine Using Snowflake & Amazon SageMaker

A library for training and deploying machine learning models on Amazon SageMaker using R through paws sdk

A project cum tutorial which will help you understand and setup your custom keras project in Amazon SageMaker.

Sentiment Analysis WebApp using LSTM, made with ❤️ in PyTorch and deployed on AWS🚀 Do 🌟 the repo and show some love.

This is a solution that allows you to offload a resource intensive Monte-Carlo simulation to more powerful machines on Amazon SageMaker, while still being able to develop your scripts in your RStudio IDE.

Hands on end to end workshop to explore Amazon SageMaker.

Automating code execution tasks on Amazon SageMaker via APIs

Projects using S3, Amazon SageMaker, AWS Lambda Function, Amazon Forecast; Projects related to SQL, Hadoop, Flink (Java), and Google Map API (Jun 2019 - Jul 2019)

How to train, deploy and monitor a XGBoost regression model in Amazon SageMaker and alert using AWS Lambda and Amazon SNS. SageMaker's Model Monitor will be used to monitor data quality drift using the Data Quality Monitor and regression metrics like MAE, MSE, RMSE and R2 using the Model Quality Monitor.

How to perform distributed training on Amazon SageMaker using SageMaker's Distributed Data Parallel library and debug using Amazon SageMaker Debugger.

Object Detection using Amazon Sagemaker from AWS

Amazon SageMaker 101 Workshop

The aim of this project is to predict the long-term price trend of two indices and one stock with at least 90% accuracy.

How to train a XGBoost regression model on Amazon SageMaker, host inference on a Docker container running on Amazon ECS on AWS Fargate and optionally expose as an API with Amazon API Gateway.