Model Integration - AIQ Capability Overview
Model Integration is the process of taking a trained model from the research and development phase and integrating it...
Model Integration is the process of taking a trained model from the research and development phase and integrating it with an application or other business workflow to provide value to end users.
To deliver value from models, companies must have the ability to integrate them into internal systems, products, and services. Companies need clearly defined objectives for applying machine learning to business needs, documented strategies for governing the use of models, and standardized processes and technologies to effectively serve model predictions within business workflows to deliver value to end users.
MLOps is the intersection of Machine Learning and DevOps. It's the technology and processes for developing, deploying, maintaining, and automating machine learning models to meet business needs at scale.
Model serving takes a developed machine learning model and makes it accessible for delivering predictions to support business products or services. This could include real-time streaming predictions through an API or batch predictions made offline.
Preprocessing is performed to prepare, clean, and organize data to make it suitable for training a machine learning model. Post processing is performed to analyze, transform, and visualize model outputs to further derive insights for business or end user needs.