MLOps is a set of software development practices that aims to build, deploy, monitor and maintain ML/AI (Machine Learning/Artificial Intelligence) models in production reliably and efficiently.
The term is derived from a contraction of machine learning and the continuous development practices of DevOps.
Ultra Tendency can guide your teams through the many different features, algorithms, modeling techniques, and parameter configurations to find what works to address your problems.
The challenge of tracking what worked and what didn't while maintaining reproducibility is much larger than traditional software engineering.
We can develop tools and processes to ensure less waste in repeated experiments while maintaining code reusability.
Deployment of ML/AI models
Integration of ML/AI models into software and hardware technology
Automation of deployment procedures
Reproducibility of data, models, and predictions
Diagnostics
Governance and regulatory compliance
Scalability
Collaboration
Monitoring and management
Business uses
Iterative progression of ML/AI development
A tricky element of ML Ops is that ML/AI is much more experimental than traditional software engineering.
An array of interconnected and illustrative case studies pertaining to this particular service.
