The MLOps Landscape
A Center-of-Excellence for deploying enterprise-wide AI at scale gives businesses a single-point resource to deploy, monitor, manage, and govern all their models in production, regardless of how they were created or when and where they were deployed. MLOps practices enable and ensure that machine learning models are not just experimental but are adopted by business users as part of routine business operations.
Course5 helps organizations make their AI systems and applications more robust by reducing errors and improving security issues. Our MLOps solution helps monitor data drift and model accuracy and automate model health monitoring and lifecycle management.
How Course5’s MLOps Solution Helps
Deploy, monitor, manage and govern ML models in production
Achieve standardization and scalability across platforms
Automatically recalibrate, retrain, and serve ML models
Deliver ML models efficiently and quickly even on collaborative projects across departments
Purpose-driven Solutions for Every Situation
The AI ecosystem and pipeline span multiple environments, including data center, cloud, developer notebooks, and platforms. Course5’s MLOps framework is technology-and platform-agnostic, focusing on standardization and scalability across platforms and environments. The end result is that training and deployment processes run smoothly.
Course5’s MLOps framework has 3 core components –
- CI (Continuous Integration) for testing and validating data, schemas, and models.
- CD (Continuous Deployment) for automatically deploying model predictive services and changes within models
- CT (Continuous Training) for automatically retraining and serving the model
Model Health Monitoring and Lifecycle Management
Course5’s MLOps solution provides constant monitoring and production diagnostics to improve the performance of your existing models. The use of best practices enables you to track model health, accuracy, and data relevance to explain why your model is degrading or underperforming. Course5’s practices give you constant evaluation and continuous learning to help you avoid surprises in model performance.