Abstract: Examples described herein include a natural language processing (NLP) workflow for determining answers to queries. A query is received from a first client of a plurality of clients. A set of machine learning (ML) models are selected based on available service provider resources for processing the query. Each of the set of ML models corresponds to a respective stage of a NLP workflow. The query is input to a first model of the set of ML models. According to the NLP workflow, results from the first model are input to a second model of the set of ML models to determine a final result. A query answer based on the final result is transmitted to the first client.

Abstract: A system for compressing a plurality of data files is provided. The system includes a data receiver configured to receive a plurality of data files. Further, the system includes a data segregation module configured to identify a set of homogeneous files from the plurality of data files. The homogeneous file includes of a static region and a dynamic region. Further, the system includes a compression engine configured to compress the set of homogeneous files into a compressed video data file using a video compression technique. Furthermore, the system may include a storage module, configured to store the compressed video data file and associated meta data. A method and a tool for compressing a plurality of data files are also provided.