An open-source, cloud-agnostic MLOps platform for the modern ML lifecycle
AWS SageMaker provides a comprehensive ecosystem for managing the entire machine learning lifecycle—from data preparation and training to deployment and monitoring. However, its adoption is constrained by high operational costs and tight coupling with AWS infrastructure.
Organizations often resort to combining open-source tools like DVC for data versioning, MLflow for experiment tracking, and Kubeflow for orchestration. While powerful individually, these tools address isolated workflow segments and lack cohesive integration, resulting in operational friction and maintenance overhead.
MLForge is an open-source, cloud-agnostic MLOps platform designed to unify the fragmented ML tooling landscape. It delivers a modular, end-to-end solution deployable on any infrastructure—on-premises, private cloud, or public cloud—without vendor lock-in.
By integrating the capabilities of specialized tools into a single coherent framework, MLForge bridges the gap between enterprise-scale functionality and accessibility, enabling organizations to manage their ML lifecycle with:
- ✅ Cost Efficiency - No vendor lock-in or premium cloud service fees
- ✅ Transparency - Full visibility into operations and workflows
- ✅ Infrastructure Control - Deploy anywhere, own everything
MLForge aims to deliver a complete MLOps platform that supports the entire machine learning workflow, from initial data ingestion through production deployment and beyond.
Unify data ingestion, preprocessing, training, evaluation, deployment, and monitoring within a single integrated platform.
Enable deployment across any infrastructure without dependencies on proprietary cloud services, ensuring true portability and vendor independence.
Implement native versioning for datasets, models, and pipelines to guarantee reproducibility and enable complete workflow traceability.
Provide systematic logging of hyperparameters, metrics, and artifacts, facilitating transparent comparison and performance analysis across experiments.
Support definition and execution of complex ML workflows with intelligent dependency resolution, scheduling, and parallel execution capabilities.
Integrate continuous integration and deployment workflows tailored specifically for machine learning, enabling automated testing, retraining triggers, and progressive rollouts.
Deliver a unified registry for managing model versions, metadata, and deployment states across multiple environments and teams.
Design for distributed training, elastic compute resources, and plugin-based extensibility to accommodate growing workloads and custom integrations.
Offer both an intuitive web-based dashboard for visual management and comprehensive APIs/SDKs for programmatic control and automation.
- Vishruth V Srivatsa
- Akhil Sakthieswaran
- Shriya Bharadwaj
- Ajitesh Kallepalli
- Abhishek Sulakhe
- Siddhanth Saha
| Feature | Description |
|---|---|
| Data Versioning | Track and manage dataset versions with Git-like semantics |
| Experiment Tracking | Log, compare, and visualize ML experiments with rich metadata |
| Pipeline Automation | Define reproducible workflows with dependency management |
| Model Registry | Centralized repository for model artifacts and metadata |
| Deployment Management | Deploy, monitor, and rollback models across environments |
| Distributed Training | Scale training across multiple nodes and accelerators |
| Web Dashboard | Intuitive UI for managing and monitoring ML workflows |
| RESTful APIs | Comprehensive APIs for programmatic access and automation |