Data pipelines for AI models that are deployed for enterprise applications would have to address key aspects such as those mentioned below, for seamlessly leveraging AI capabilities. Aganitha’s AI Data Manager enables a seamless data pipelining for AI models.
Read more about how AI Data Manager addresses key aspects related to data pipelining for enterprise AI models in our detailed whitepaper.
AI Models need data to be curated before consumption
Data is generated in different formats: Structured data from enterprise applications, Unstructured data such as images, text, audio, video files from social media, call centre operations, customer engagement channels etc. Aganitha’s accelerator automate the data access to both internal (enterprise corpus, private data etc.) and external (public databases, paid journals, articles, research papers etc.) datasources, extract relevant information and synthesize the data for consumption. All this while adhering to enterprise policies!
Consolidating the diverse data after necessary transformations and normalizations into formats that AI models can consume requires extensive planning such as: identifying steps that need to be modularized, identifying intermediate features that are to be stored etc.
Data pipelines should be able to manage data explosion
Data cleansing and pre-processing techniques such as missing value treatment, outlier identification etc. are necessary to be carried out before feeding the data to AI models. Managing this real time on a huge volume of data is a challenge.
Updating datasets with latest and relevant curated data is required to facilitate on demand update of models and real-time utilization of data.