MLWatch is an open-source observability and monitoring tool designed specifically for machine learning (ML) and artificial intelligence (AI) workloads. It enables real-time tracking of model performance, data pipeline health, and resource utilization to ensure smooth and optimized operations in production environments.
- Framework Integration: Seamlessly integrates with popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Real-Time Monitoring: Offers real-time metrics on training performance, inference latency, and resource consumption.
- Data Pipeline Health: Monitors data pipeline stages to detect bottlenecks, data skew, and inconsistencies.
- Model Performance Tracking: Tracks key ML metrics like accuracy, precision, recall, and more to identify drift and anomalies.
- Scalability: Scalable architecture using cloud-native technologies such as Kubernetes, making it suitable for both small and large deployments.
- Alerting & Notification: Configurable alerts for anomalies, performance degradation, and resource issues.
MLWatch bridges the gap between traditional observability tools and the unique needs of AI/ML operations. It provides comprehensive visibility into not only infrastructure but also the specific ML components, ensuring that your models perform as expected while minimizing operational overhead. With advanced visualization, alerting, and reporting, MLWatch helps teams proactively address issues before they impact end-users.
- Install MLWatch using
poetryor your preferred package manager. - Configure your environment by specifying the models and data pipelines to be monitored.
- Deploy in your cloud-native environment using Helm charts or Docker for easy integration.
- Support for additional frameworks (e.g., Apache MXNet, ONNX).
- Distributed Training Monitoring for large-scale model training.
- Explainability Integration with tools like SHAP and LIME.
We welcome contributions from the community! See our CONTRIBUTING.md for guidelines on how to get involved.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.