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active-boy-kubernetes

All about deployments and integrations.

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amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

april-cohort

Of Github

april_assignments

This repository will contain assignments through the course work.

ar-cptext

AR Copy Paste - Text Proto

autogluon

AutoGluon: AutoML for Image, Text, and Tabular Data

aws-doc-sdk-examples

Welcome to the AWS Code Examples Repository. This repo contains code examples used in the AWS documentation, AWS SDK Developer Guides, and more. For more information, see the Readme.md file below.

aws-elastic-beanstalk-express-js-sample

Sample web application for the AWS DevOps Engineer Learning Path.

aws-machine-learning-university-accelerated-cv

Machine Learning University: Accelerated Computer Vision Class

azure-stream-analytics

Azure Stream Analytics

azureml-bert

End-to-End recipes for pre-training and fine-tuning BERT using Azure Machine Learning Service

azureml-examples

Official community-driven Azure Machine Learning examples, tested with GitHub Actions.

big-data

Big Data NLP

cli

GitHub’s official command line tool

crisislex

CrisisLex: Your data and lexical resource in crises

dash-demo-renderer

This is a demo

dash-sample-apps

Open-source demos hosted on Dash Gallery

datasets

Datasets used in Plotly examples and documentation

datasets-1

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

dbx-streaming-webinar

Source code for December Webinar with Databricks

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deeplearningexamples

Deep Learning Examples

dev-dash

Render

edw-best-practices

Git Repo for EDW Best Practice Assets on the Lakehouse

elastic-beanstalk-samples

This repository contains code and configuration samples (e.g. .ebextensions) for AWS Elastic Beanstalk.

evaluate

A library for easily evaluating machine learning models and datasets.