![]() ![]() In Konan, the API documentation is created for deployments, specifying how to interact with the model. API Documentation Īn API document is used to communicate how to use an API by defining the expected inputs and outputs. The document that describes this interface is called API Specification or API Documentation. API Īpplication Programming Interface (API) is an interface provided by your application to allow other apps/programs to communicate with it. It provides the necessary REST API endpoints that make the model accessible. Deployment Ī deployment is the entity you communicate with to interact with your production-ready machine learning model. Core Concepts Model Ī model in Konan is your trained model containerized using the requirements specified in the Konan documentation. Konan allows the team of data scientists to focus on building great machine learning models and not worry about deploying and managing them. It allows users to have their models up and running in production with the bare minimum requirements. ![]() Konan is an MLOps platform that aims to decrease time to deployment as well as provide maintenance and monitoring out-of-the-box, all while being language and platform agnostic. This need gave a rise to Machine Learning Operations (MLOps) a set of practices that streamline the process of deploying, monitoring and maintaining machine learning models in production. ![]() A different skill-set is required to deploy and monitor the models as well as provision and maintain the required infrastructure. Moving those models to production to be used and integrated into bigger systems is a different story. Data scientists are experts when it comes to building state of the art machine learning solutions. ![]()
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January 2023
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