MLOps-pipeline
ML pipeline
Automates the end-to-end machine learning workflow from code commit to model deployment
18 stars
4 watching
13 forks
Language: Python
last commit: over 5 years ago Related projects:
Repository | Description | Stars |
---|---|---|
oxid15/cascade | A lightweight MLOps library for small teams and individuals to manage machine learning model development lifecycle | 22 |
seldonio/tempo | An MLOps Python library that enables data scientists to deploy and orchestrate machine learning pipelines for production-ready inference. | 116 |
mlrun/mlrun | A platform for managing and deploying machine learning workflows across the application lifecycle | 1,446 |
omegaml/omegaml | An end-to-end MLOps platform that streamlines machine learning development, deployment, and collaboration. | 95 |
combust/mleap | Enables deployment of machine learning data pipelines and algorithms to production | 1,504 |
sematic-ai/sematic | An open-source platform for building and managing machine learning pipelines with Python | 974 |
visenger/mlops | Provides end-to-end examples and solutions for operationalizing ML workflows with Azure Machine Learning | 3 |
minyus/pipelinex | A Python package to build and experiment with machine learning pipelines using Kedro, MLflow, and other tools | 224 |
polyaxon/polyaxon | A platform for managing and orchestrating the machine learning lifecycle | 3,571 |
jvalegre/robert | Automated machine learning protocols for cheminformatics using Python | 38 |
star-whale/starwhale | An MLOps platform providing tools and services for efficient machine learning development and deployment | 208 |
tdcox/mlops-roadmap | Collaborative effort to define and document the key milestones and strategies for adopting and implementing Machine Learning Operations (MLOps) practices in software development | 16 |
musket-ml/segmentation_training_pipeline | A tool for defining and running machine learning experiments for image segmentation in Python. | 53 |
paulescu/hands-on-train-and-deploy-ml | A step-by-step guide to building and deploying a Machine Learning-based REST API for predicting crypto prices using Python. | 758 |
mlcommons/inference | Measures the performance of deep learning models in various deployment scenarios. | 1,236 |