Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
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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.
MLOps workshop with Amazon SageMaker
Deploy FastAI Trained PyTorch Model in TorchServe and Host in Amazon SageMaker Inference 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.
Detect Defects in Products from their Images using Amazon SageMaker
End to end Machine Learning with Amazon SageMaker
Deploy Stable Diffusion Model on Amazon SageMaker Endpont
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.
My Projects Submission to Udacity's Deep Learning Nanodegree Program
Build end-to-end Machine Learning pipeline to predict accessibility of playgrounds in NYC
Twin Neural Network Training with PyTorch and fast.ai and its Deployment with TorchServe on Amazon SageMaker
Deep Learning Udacity Nanodegree - SageMaker Deployment of a Sentiment Analysis model
This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.
This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD
In this repo, we show how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint.
This repo contains demo code for reInvent2021 session AIM408 Achieve high performance and cost-effective model deployment
Project from Deep Learning Nanodegree - Udacity
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