107 Open source projects that are alternatives of or similar to aml-compute

GitHub Action that allows you to create or connect to your Azure Machine Learning Workspace.

GitHub Action that allows you to deploy machine learning models in Azure Machine Learning.

GitHub Action that allows you to register models to your Azure Machine Learning Workspace.

JavaScript example illustrating how to use the Maritime Chart Service functionality provided by ArcGIS for Maritime: Server.

Slack bot for monitoring your Metaflow flows!

Serving PyTorch models with TorchServe 🔥

📒 Experiment tracking tool and model registry

My EFI folder for Ryzen Hackintosh, as known as Ryzentosh.

FuseML aims to provide an MLOps framework as the medium dynamically integrating together the AI/ML tools of your choice. It's an extensible tool built through collaboration, where Data Engineers and DevOps Engineers can come together and contribute with reusable integration code.

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

A sample Azure Machine Learning project for Transfer Learning-based custom image recognition by utilizing Keras.

Kedro Plugin to support running pipelines on Kubernetes using Airflow.

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

🎲 A curated list of MLOps projects, tools and resources

The collaboration workspace for Machine Learning

Customer registry supporting Know Your Customer (KYC), Customer Identification Program (CIP), and OFAC checks

MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.

The PHP SDK for interacting with the Yoti Platform

CartPole game by Reinforcement Learning, a journey from training to inference

An Azure Databricks workshop leveraging the New York Taxi and Limousine Commission Trip Records dataset

Evaluate wandb, tensorboard, neptune, mlflow, etc

Polyaxon Core Client & CLI to streamline MLOps

Model Serving Made Easy

Subscribe to this GitHub repo to access the latest tech talks, tech demos, learning materials & modules, and developer community updates!

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Feature Store for Machine Learning

Kraken CI is a continuous integration and testing system.

This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.

Deployment template for a continuous training pipeline.

Black-box optimization library

Monitor the stability of a Pandas or Spark dataframe ⚙︎

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

HI-ML toolbox for deep learning for medical imaging and Azure integration

PyTorch Lightning + Hydra. A very user-friendly template for rapid and reproducible ML experimentation with best practices. ⚡🔥⚡

Algorithm engineering is hard enough: don't spend your time with logistics. QA-Board organizes your runs and lets you visualize, compare and share results.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Learn how to responsibly deliver value with ML.

AML/CTF/KYC/OFAC Search of global watchlist, sanctions, and politically exposed person (PEP)

Portable automation meta-framework to manage, describe, connect and reuse any artifacts, scripts, tools and workflows on any platform with any software and hardware in a non-intrusive way and with minimal effort. Try it using this tutorial to modularize and automate ML Systems benchmarking from the Student Cluster Competition at SC'22:

MLOps Case Study

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…

3D Passive Face Liveness Detection (Anti-Spoofing) & Deepfake detection. A single image is needed to compute liveness score. 99,67% accuracy on our dataset and perfect scores on multiple public datasets (NUAA, CASIA FASD, MSU...).

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

Coarse-grained lineage and tracing for machine learning pipelines.

Deploying a Batch Scoring Pipeline for Python Models

A Collection of GitHub Actions That Facilitate MLOps

BentoML Example Projects 🎨

[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

Helm charts for creating reproducible and maintainable deployments of Polyaxon with Kubernetes.

Machine Learning Pipelines for Kubeflow

Public reusable components for Polyaxon

A lightweight tool to get an AI Infrastructure Stack up in minutes not days. K3ai will take care of setup K8s for You, deploy the AI tool of your choice and even run your code on it.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Microsoft buduje i tworzy Polską Dolinę Cyfrową. W ramach tej inicjatywy podjęliśmy się wyzwania zbudowania chmurowych kompetencji wśród 150tys osób w Polsce. Jednym z elementów tej inicjatywy jest dedykowany kurs na studiach inzynierskich i magisterskich na Politechnice Warszawskiej poświęcony chmurze obliczeniowej oraz sztucznej inteligencji.

A tool for training models to Vertex on Google Cloud Platform.

Azure Machine Learning Cheat Sheets

This repository contains the code for the blogpost: How to Develop a Currency Detection Model using Azure Machine Learning

A GitHub Action that makes it easy to use Great Expectations to validate your data pipelines in your CI workflows.

Projects developed by Domino's R&D team