Project that uses AWS SageMaker to train a neural network and serve the model
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Updated
Mar 29, 2020 - Jupyter Notebook
Project that uses AWS SageMaker to train a neural network and serve the model
A list of interesting bookmarks, blogs, channels, papers, and anything that can be considered as a day to day reference for an AI, DS, ML, or DL practioner. [WIP]
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud
This machine learning pipeline project aims to develop an ML model to identify bank customer churn.
This machine learning pipeline project aims to develop an ML model to identify customer sentiment from French-language tweets on social media.
These are my personal notes regarding Machine Learning DevOps stuff
A simple Python example of a Model Service that can be fronted by the Model Sidecar
ORBIT SMKN 4 Bandung team repository for Turnamen Sains Data Nasional 2022 coordinated by Cybertrend Data Academy and Asosiasi Data Sains dan AI Indonesia with supported by several government agencies and universities in Indonesia.
Slides of my talk "Is Your ML Model Trustworthy?" at the MLOps World Conference on the 16th of June 2021.
Sample Airflow ML Pipelines
A library of computer vision models and a streamlined framework for training them.
Data Science Experiments Repository of Ideas2IT
interactive coding environment for microservices demo
Demo usage of Weights & Biases for ML Ops
Raccogliamo qui tutti i link alle risorse menzionate durante i nostri QShare
This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.
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