184 Open source projects that are alternatives of or similar to tfx-kubeflow-pipelines

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.

Machine Learning Platform for Kubernetes (MLOps tools for experimentation and automation)

A declarative KubeFlow Management Tool

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI

PipelineAI Kubeflow Distribution

Machine Learning Pipelines for Kubeflow

End-to-end pipeline with TFX to train and deploy a BERT model for sentiment analysis.

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.

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

Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.

Build container images with Knative + Gitlab + Harbor inside Kops cluster running on AWS

Lightweight and Fast Feature Store Powered by Go (and Rust).

Serverless Inferencing on Kubernetes

Combinator.ml's central repo, documentation and website

Tibanna helps you run your genomic pipelines on Amazon cloud (AWS). It is used by the 4DN DCIC (4D Nucleome Data Coordination and Integration Center) to process data. Tibanna supports CWL/WDL (w/ docker), Snakemake (w/ conda) and custom Docker/shell command.

A project-based course on the foundations of MLOps with a focus on intuition and application.

The Fuzzy Labs guide to the universe of open source MLOps

🏵 Gee is tool of stdin to each files and stdout. It is similar to the tee command, but there are more functions for convenience. In addition, it was written as go

A set of demo of deploying a Machine Learning Model in production using various methods

Handles OAuth and stores slack token

"Integrating Julia in real-world, distributed pipelines" for JuliaCon 2017

The Refreshingly Simple Cross-Platform C++ Dataflow / Pipelining / Stream Processing / Reactive Programming Framework

A collection of WhyLogs examples in various languages

K3ai-core is the core library for the GO installer. Go installer will replace the current bash installer

Engine for ML/Data tracking, visualization, dashboards, and model UI for Polyaxon.

A cookiecutter for projects with continuous delivery baked in.

Crane is a easy-to-use and beautiful desktop application helps you build manage your container images.

❄️ 🐳 Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc

Python Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deployment as a RESTful service on Kubernetes.

System.IO.Pipelines API adapter for System.Net.WebSockets

Open resource exchange platform for non-human primate neuroimaging

A guideline for basic use and installation of kubeflow in AWS.

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

The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.

Machine Learning Operator & Controller for Kubernetes

Docker images for fastai

FakeFinder builds a modular framework for evaluating various deepfake detection models, offering a web application as well as API access for integration into existing workflows.

Serving TensorFlow models with TensorFlow Serving 📙

Kubernetes-friendly ML model management, deployment, and serving.

A process management system written in python

Project template for developing shared Jenkins pipeline libraries.

Operationalize a video anomaly detection model with Azure ML

Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

All of my recommendations for aspiring engineers in a single place, coming from various areas of interest.

A library for managing, validating, summarizing, and visualizing data.

TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.

Completely Scalable FastAPI based template for Machine Learning, Deep Learning and any other software project which wants to use Fast API as an API framework.

Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.

A collection of portable workflows, automation recipes and components for MLOps in a unified CK format. Note that this repository is outdated - please check the 2nd generation of the CK workflow automation meta-framework with portable MLOps and DevOps components here:

This repository provides AI/ML service(MachineLearning model serving) modernization solution using Amazon SageMaker, AWS CDK, and AWS Serverless services.

Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.

A task for Azure DevOps Pipelines to run newman tests.

Azure AKS Kubernetes Masterclass

a simplified machine learning container platform that helps teams get started with an automated workflow

ADENINE: A Data ExploratioN PipelINE

The MLOps Workload Orchestrator solution helps you streamline and enforce architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services.

Python package for Customizable Data Preprocessing Pipelines

MLOps template with examples for Data pipelines, ML workflow management, API development and Monitoring.