MulticoreWare engagement building end user applications spans across from traditional Computer Vision (CV) based applications to Deep Learning (DL) Applications a myriad of verticals. In case of CV applications, MulticoreWare has deployed solutions with the building blocks like object detection, object identification, object classification to support various inference engines for further actuation. Some of the work involves video motion analysis, image segmentation, scene (3D) reconstruction to image restoration using machine learning based filters.

• On Premises AI Solution (On Prem) – Use deploys and manages the infrastructure components (ML platform, algorithms, compute, and data). User has total control and everything in-house.
• AI Infrastructure as a Service (AI-IaaS) – User brings their models, algorithms, data types & compute resources. However, ML capabilities are leveraged from Cloud service providers (Google Cloud ML Engine, Amazon ML Engine). User builds and trains ML models, but leases ML platform infrastructure components.
• Managed AI as a Service (Managed AI-IaaS) – User leverages the platform ML capabilities with their own data types. The cloud service providers like Google Cloud AutoML, Amazon Sagemaker, Azure ML Studio are few who offer Managed AI-IaaS.
• Platform as a Service (PaaS) – User focuses on business value while the platforms provide the cognitive capabilities of a platform as a service.

MulticoreWare offers consultancy services in any of the consumption model based on the application needs and in-house ML expertise.