A machine learning pipeline is the end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models). It includes raw data input, features, outputs, the machine learning model and model parameters, and prediction outputs.
The design and implementation of a machine learning pipeline is at the core of enterprise AI software applications and fundamentally determines the performance and effectiveness. In addition to the software design, additional factors must be considered, including choice of machine learning libraries and runtime environments (processor requirements, memory, and storage).
Many real-world machine learning use cases involve complex, multi-step pipelines. Each step may require different libraries and runtimes and may need to execute on specialized hardware profiles. It is therefore critical to factor in management of libraries, runtimes, and hardware profiles during algorithm development and ongoing maintenance activities. Design choices can have a significant impact on both costs and algorithm performance.
C3 AI Suite and C3 AI Applications provide sophisticated capabilities to build, manage, and operate robust machine learning pipelines. In addition, C3 AI software provides advanced capabilities to automatically provision and manage infrastructure services of leading cloud providers including Azure, AWS, and Google, as well as private and hybrid cloud environments.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.