mlcomp
Pipeline manager
A distributed framework for building and managing complex machine learning pipelines with a user-friendly interface.
Distributed DAG (Directed acyclic graph) framework for machine learning with UI
188 stars
14 watching
27 forks
Language: Python
last commit: over 6 years agoartificial-intelligenceautomlcomputer-visiondeep-learningdistributed-computinginfrastructuremachine-learningpythonpytorchresearch
Related projects:
| Repository | Description | Stars |
|---|---|---|
| An MLOps Python library that enables data scientists to deploy and orchestrate machine learning pipelines for production-ready inference. | 117 | |
| A framework for defining and automating bioinformatics pipelines using Nextflow. | 44 | |
| A tool for managing data science pipelines by automating build, testing, and deployment processes while ensuring correctness and performance. | 58 | |
| An MLOps framework that allows developers to define and deploy machine learning workloads on any cloud infrastructure using a Python native API. | 520 | |
| Automated machine learning framework using JSON syntax to define and generate custom pipelines with pre-processing, feature engineering, and model building steps. | 453 | |
| Enables deployment of machine learning pipelines from Spark and Scikit-Learn to production | 1,506 | |
| A framework for building and managing CI/CD pipelines and application environments with cryptographic signed dependencies. | 461 | |
| A real-time online machine learning library built on top of Storm and Trident. | 381 | |
| A Python library for managing and optimizing computational workflows with parallel processing and data reuse. | 22 | |
| A lightweight logger for machine learning experiments | 127 | |
| An MLOps platform providing tools and services to deploy, collaborate and manage machine learning models and data pipelines in a simplified way | 96 | |
| A platform for data-intensive scientific analysis and workflow management | 1,431 | |
| A lightweight MLOps library for small teams and individuals to manage machine learning model development lifecycle | 22 | |
| Automates the end-to-end machine learning workflow from code commit to model deployment | 18 | |
| Collaborative community for practitioners and experts in managing the end-to-end lifecycle of machine learning projects | 607 |