Introduction
ML Aide's purpose is to make model management a joy for you!
ML Aide simplifies the documentation process and tracks all relevant information of machine learning model creation. It makes model lifecycle management a joy and enables machine learning operations. ML Aide is deployable anywhere and easy to integrate into the existing enterprise software landscape.
Key Benefits
ML Aide is designed and developed to be
- Enterprise-ready: Identity, security, and integrity as a first-class citizen
- Diverse: Supports every ML library for complete freedom
- Transparent: Provided open source for maximum transparency
- Independent: Runs on every cloud platform or on-premises for complete independence
- Scalable: Scalable from single user to large enterprises and ready for growing demands
- Effective: Accelerates MLOps for more focus on what really matters: Your business
Key Features
Track all relevant information of your machine learning models with ML Aide to manage your model lifecycle from training to retirement.
- Experiment Tracking: Track parameters, metrics, and artifacts in your machine learning experiments that are organized by single runs.
- Artifact Management: Attach artifacts like code, configs, or models to your experiment runs and reuse them in your next run.
- Experiment Lineage: Inspect your experiment with a visualized lineage representing the relationship between all runs and artifacts.
- Model Staging: Put your models under version control and stage them to obtain transparency and reproducibility in your operations.
- Run Evaluation: Evaluate your runs by viewing or comparing parameters and metrics to identify the best model for your machine learning product.
- ML Library Integration: An increasing number of integrated machine learning libraries for convenient in-code tracking of parameters, metrics, and models.
- Access Management: Manage access to your machine learning projects and collaborate with other members of your team.
Getting Started
Run ML Aide in your preferred environment.
Start on localhost Start on Kubernetes
Tutorial
Walk through the model development process with ML Aide.
Essentials
Learn the essentials of ML Aide.
API Reference
Explore the Python SDK or REST API Reference.