Machine Learning Operations (MLOps)
What is Machine Learning Operations?
Machine Learning Operations (MLOps) is the practice of deploying, monitoring, and managing machine learning models efficiently in production, particularly on Edge AI devices. Also known as AI operations, it ensures seamless integration of AI models into real-time, decentralized computing environments.
Why Is It Used?
MLOps is used to maintain high-performing AI systems at the edge, enabling faster decision-making, model updates, and continuous monitoring while reducing latency and operational complexity.
How Is It Used?
Automates deployment of AI models to edge devices.
Monitors model performance and data drift in real time.
Ensures version control, scalability, and compliance.
Types of Machine Learning Operations
Continuous Integration (CI): Automates testing and updates for ML models.
Continuous Deployment (CD): Seamlessly pushes models to edge devices.
Monitoring & Governance: Tracks model performance and ensures compliance.
Benefits of Machine Learning Operations
Reduced latency for real-time AI decision-making.
Scalable and consistent model deployment across multiple edge nodes.
Faster adaptation to changing data without central server dependency.
Improved operational efficiency and lower maintenance costs.