mltrace
Pipeline analyzer
A tool to help analyze and debug machine learning pipelines by tracking the flow of data and components through the pipeline.
Coarse-grained lineage and tracing for machine learning pipelines.
468 stars
6 watching
29 forks
Language: Python
last commit: over 2 years ago
Linked from 1 awesome list
devopsmachine-learningmlopspipeline-managementtracing
Related projects:
Repository | Description | Stars |
---|---|---|
| A Python package to build and experiment with machine learning pipelines using Kedro, MLflow, and other tools | 226 |
| An MLOps Python library that enables data scientists to deploy and orchestrate machine learning pipelines for production-ready inference. | 117 |
| Automates the end-to-end machine learning workflow from code commit to model deployment | 18 |
| A lightweight logger for machine learning experiments | 127 |
| A workflow engine for unifying feature engineering and machine learning operations in data analysis pipelines | 1 |
| A software package that automates and streamlines neuroimaging data analysis workflows using a comprehensive pipeline system. | 72 |
| Enables deployment of machine learning pipelines from Spark and Scikit-Learn to production | 1,506 |
| A distributed framework for building and managing complex machine learning pipelines with a user-friendly interface. | 188 |
| A tool for tuning complex data transformation pipelines in machine learning models | 18 |
| Automated machine learning tool for tabular data pipelines | 343 |
| A lightweight tool for managing and versioning large data alongside source code in data pipelines | 184 |
| An implementation of a multi-task deep morphological analyzer with neural models and post-processing tools for natural language processing tasks. | 1 |
| A tool that enables data manipulation and analysis pipelines to be flexible, reusable, and reproducible in different environments | 89 |
| A tool for managing data science pipelines by automating build, testing, and deployment processes while ensuring correctness and performance. | 58 |
| A lightweight MLOps library for small teams and individuals to manage machine learning model development lifecycle | 22 |