deploy and manage your own ML time series model(using LSTM) with CloudFormations, Lambda, S3, SageMaker

Demonstration of a MLOps workflow with a Human Activity Recognition project with AWS SageMaker

Example Python smaples for running your local code as a SageMaker training job using @Remote decorator

Repo used for Sagemaker build

A repo to train a Custom Deep Learning Model with Amazon SageMaker

Exploring Cloud NoSQL Databases in Content Management Systems

A repo containing end to end sagemaker mlops pipeline for model building

How to use different AWS frameworks together

A Sagemaker e2e multi-model pipeline that can tune multiple models on separate datasets and deploy them to a single endpoint.

Basic Image classifer used for learning ML Life cycle using MLflow and Sagemaker

Deploy various ML models over the cloud or on the edge

Deploy GPT-2 PyTorch model with HuggingFace pretrained weights to AWS SageMaker

AccIo - Enterprise LLM : Unifying intelligence at your command!

Natural Language Processing

Elastic Data Factory

API for Continue.dev extension to support LLM models hosted via AWS Sagemaker

End-to-end MLOps solution for model retraining, training metric alerts via email, and ML model deployment to Amazon Sagemaker. Includes a CloudFormation script for infrastructure setup and a web app for visualizing history model training metrics and performing inference.

Face Detection with AWS Sagemaker

I did a simple test to see how deploying a machine learning model on AWS Sagemaker and thus turning it into an API works. Since scikit-learn models require less dependencies than e.g. TensorFlow models I went with them for this test. To do so I used a tutorial.

In this workshop you will learn how to train, deploy and host your models on AWS.