(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.

Amazon SageMaker Local Mode Examples

A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)

MLOps workshop with 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 AI collection of examples, code samples and recipes.

Hands-on demonstrations for data scientists exploring Amazon SageMaker

⛳️ PASS: Amazon Web Services Certified (AWS Certified) Machine Learning Specialty (MLS-C01) by learning based on our Questions & Answers (Q&A) Practice Tests Exams.

Deploy Generative AI models from Amazon SageMaker JumpStart using AWS CDK

Demonstration of Natural Language Query (NLQ) of an Amazon RDS for PostgreSQL database, using SageMaker JumpStart, Amazon Bedrock, LangChain, Streamlit, and Chroma.

Hands-on end-to-end workshop to explore Amazon SageMaker.

Docker images that replicate the Amazon SageMaker Notebook instance.

Amazon SageMaker Managed Spot Training Examples

'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker

End to end Machine Learning with 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.

Running your TensorFlow models in Amazon SageMaker

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

Amazon SageMaker で MLOps (前処理・学習・評価・推論、および、実験・モデル・ワークフローの管理) を実現するミニマムなコードサンプル

@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

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.

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 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.

Deploy and invoke Stability AI's Stable Video Diffusion XT (SVT-XT) 1.1 foundation model on Amazon SageMaker.

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

Build a Full stack Q&A Chatbot with Langchain, and LLM Models on Amazon Sagemaker

This repo contains demo code for reInvent2021 session AIM408 Achieve high performance and cost-effective model deployment

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.