The AI models are tested using production data and deployed after standardizing and optimizing them for the targeted deployment environment. Further the models are operationalized and monitored using right tools and frameworks.
Model Optimization – The Deep Learning Models have billions of trained parameters . It is imperative to deploy these models on less resource intensive systems which are cost effective and on edge devices where faster prediction happens. Our AI Engineers have the right frameworks and tools to perform the model optimization faster.
Model Deployment – ML Models are deployed on different environments such as on premise , cloud and edge devices . Models are expected to produce faster inferencing on an acceptable hardware specification. Our AI Engineers consider ,propose and implement the right deployment architecture for the clients.
MLOps – MLOps unifies the process of machine learning artifacts and software application release . It enables automated testing of machine learning artifacts . Our AI Engineers with the help of right tools, framework and accelerators enable MLOps in an efficient manner.