Machine learning operations or MLOps aims to more efficiently scale from a proof of concept or pilot project to a machine learning workload in production.
Implementing MLOps helps you to make your machine learning workloads robust and reproducible. For example, you'll be able to monitor, retrain, and redeploy a model whenever needed while always keeping a model in production.
The purpose of MLOps is to make the machine learning lifecycle scalable:
- Train model
- Package model
- Validate model
- Deploy model
- Monitor model
- Retrain model
GenAIOps (Generative AI Operations) is an evolving framework that builds upon established frameworks like DevOps, MLOps, and LLMOps. It is designed to equip teams with the adaptive culture, methodologies, and principles required to effectively address the distinctive complexities, scalability needs, and security challenges posed by generative AI throughout the development and operational lifecycle.
The Development and Operation of Generative AI must be trustworthy, effective, and rooted in accountability.
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