Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
-
Updated
Jul 19, 2022 - Jupyter Notebook
Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.
Detect Defects in Products from their Images using Amazon SageMaker
MLOps workshop with Amazon SageMaker
This workshop will familiarize you with some of the key steps towards building an end-to-end predictive maintenance system leveraging Amazon SageMaker, Amazon Polly and the AWS IoT suite.
Project from Deep Learning Nanodegree - Udacity
End to end Machine Learning with Amazon SageMaker
My Projects Submission to Udacity's Deep Learning Nanodegree Program
Deploy FastAI Trained PyTorch Model in TorchServe and Host in Amazon SageMaker Inference Endpoint
Deep Learning Udacity Nanodegree - SageMaker Deployment of a Sentiment Analysis model
Build end-to-end Machine Learning pipeline to predict accessibility of playgrounds in NYC
Deploy Stable Diffusion Model on Amazon SageMaker Endpont
In this repo, we show how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint.
This is a short example showing how to utilize Amazon SageMaker's real time endpoints with OpenAI's open source Whisper model for audio transcription.
Udacity Deep Learning Nanodegree Projects
Workshop for running HuggingFace Models on Amazon SageMaker.
As part of the ML Engineering course from Udacity, I used AWS SageMaker and S3 resources to develop a price forecaster for stocks.
Twin Neural Network Training with PyTorch and fast.ai and its Deployment with TorchServe on Amazon SageMaker
Add a description, image, and links to the sagemaker-deployment topic page so that developers can more easily learn about it.
To associate your repository with the sagemaker-deployment topic, visit your repo's landing page and select "manage topics."