It is becoming very challenging to co-design, optimize and deploy efficient AI Systems in the real world: "MLOps Is a Mess But That's to be Expected".
However, our experience suggests that it is possible to apply DevOps principles to MLOps if we organize all AI, ML and Systems artifacts including models, data sets, frameworks, libraries, tools and scripts as a database of unified components with a common API and extensible meta description that describe dependencies on other artifacts, operating systems and hardware.
We are prototyping CM-based automations to convert native user scripts and artifacts into portable CM scripts that can help to modularize AI, ML and other complex applications and automatically adapt them to diverse and rapidly evolving software and hardware stacks.
For example, we use CM scripts to enable collaborative, deterministic and reproducible benchmarking, co-design, optimization and deployment of AI and ML Systems. across continuously changing software, hardware, models and data sets.
Install the CM toolkit as described here.
Use CM to install this repository on your system:
$ cm pull repo octoml@cm-mlopsYou can now list available CM scripts as follows:
$ cm list scriptYou can run any CM script as follows:
$ cm run script {CM script alias or UID}Follow the CM scripts tutorial to understand CM concepts.