By automating the manual, time consuming data management tasks, Akridata allows data scientists to spend more time building better AI models faster, and less time on labor-intensive tasks like finding, cleaning, and reorganizing huge amounts of data. Add to that better IT efficiency and lower costs and the result is a 2x improvement in data scientist productivity.
Access the right data in minutes or hours vs. hours or days
Free up time for critical tasks
Efficiently handle growing data volumes at scale
Akridata pre-processes, catalogs, and prioritizes unstructured data at the edge to get relevant data more quickly, which helps train AI models more accurately. Only Akridata can browse, search, and access specific data regardless of where it resides (edge, core, cloud) and regardless of its geography, storage tier, version, etc. — undeterred by constantly changing data relevancy or evolving data pipelines.
Train and iterate AI models faster
Improved accuracy, improved safety
Access at scale, anytime, anywhere
Akridata lowers costs by enabling more efficient reuse of existing infrastructure and software assets, avoiding unnecessary costs in data storage and transfer, and improving data scientist productivity. We call this “smart processing at the edge” because data science teams can immediately focus on the 1%-10% of data that is of value which significantly reduces the time and cost required to access, identify, process, transfer, and store data.
Avoid moving large amounts of data to the cloud
Reduce spend on storage/compute
Decrease bandwidth and latency
Akridata allows organizations to extend their existing infrastructure with an Edge to Core to Cloud AI Data Ops platform that retains your hardware investments, processes, and applications. The Akridata platform is also completely customizable to support your unique requirements.
Leverage existing investments
Data tracking is necessary for debugging models and forensic analysis of data. Akridata treats data as code, which gives it the unique ability to track and trace data lineage/versioning from source to AI model to production use in the field. Tracking data lineage provides support for enforcing and verifying regulatory compliance with incidence management and data privacy regulations such as GDPR (EU), CCPA (California) as well as industry standards like HIPAA, FACTA, and GLBA.
Retrieve data anywhere, anytime
Track data lineage and provenance for each data object in the system