The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps

This article provides an overview of the Collective Knowledge technology (CK cKnowledge). CK attempts to make it easier to reproduce ML&systems research, ML models in production, and adapt them to continuously changing data, models, research techniques, software, and hardware. The CK concept is to complex systems and ad-hoc research projects into reusable-components with unified APIs, CLI, and JSON meta description. Such can be connected into portable workflows using DevOps principles with reusable automation actions, software detection plugins, meta, and exposed optimization parameters. CK workflows can automatically in different models, data and tools from different vendors while building, and benchmarking research code in a unified way across diverse and environments. Such workflows also help to perform whole system, reproduce results, and compare them using public or private on the CK platform (https: //cKnowledge. io). For example, the CK approach was successfully validated with industrial partners to co-design and optimize software, hardware, and machine learning for reproducible and efficient object detection in terms of speed, , energy, size, and other characteristics. The long-term goal is to and accelerate the development and deployment of ML models and systems helping researchers and practitioners to share and reuse their knowledge, , best practices, artifacts, and techniques using open CK APIs.