InfoFrames
Fast, powerful, scalable, and programmable library for
Big Data storage and analysis.
InfoFrames store data in the compressed form of effective summaries that AI/ML algorithms can take advantage of.
Ability to take full advantage of unstructured data.
About
of the data we generate is unstructured.
But only
of organizations are able to take advantage of such data.
- Yet, leveraging unstructured data can give companies a competitive edge. Thanks to InfoFrames’ native Tensor data type, unstructured data such as images and videos can easily be stored inside data bases in the form of (compressed) tensors. That provides enablement for various new types of analytics on such data.
- In a dynamic world, new categories, trends, phenomena emerge much more rapidly than before and the ability to quickly identify ‘the new’ is priceless. This cannot be achieved with metadata solely. The description, representation of the world in the analytic databases only in the metadata language is not enough. InfoFrames provide the in-database information layer which is between original, information-rich but hardly manageable raw unstructured data and the derived layer of semantic indexes (or metadata).
- Training large Machine Learning models can be resource-consuming, whether it is time, computational power or data. That means expensive. InfoFrames rapidly reduce the amount of resources, especially data and thus time, needed to obtain high quality ML models.
InfoFrames will help you with:
01.
Storing data in efficient, compressed form that allows for using them in machine learning and data science applications as-is
02.
Storing and analyzing large sets of multi-dimensional data (up o 30x better than NumPy)
03.
Data is stored in the compressed form of effective summaries
How does it work?
InfoFrames is a library that provides APIs in C++ and Python for storing and processing big data. Data is stored in the compressed form of effective summaries that support advanced AI/ML algorithms.
Features:
Fast I/O operations
Support for multiple common data types
Support for tensor data type and their efficient storage and processing
ML/AI methods supported by efficient data summaries
Fast I/O operations
Support for tensor data type and their efficient storage and processing
Features:
