GTC talk: Accelerate Autonomous Vehicle Development with NVIDIA DRIVE on Microsoft Azure
One of the Top-5 Talks at GTC’22
This session demonstrates how Microsoft Azure’s HPC/AI Infrastructure and services can accelerate and operationalize end-to-end development of ADAS systems. We will walkthrough AVOps on Microsoft Azure partnering with Akridata.
– Adithya Ranga, Senior Program Manager, Microsoft Corporation
– Kunal Vasavada, Senior Director of Platform Engineering & Solutions, Akridata
Simplify AI data operations, ingest and organize streams of data, including from tape, from edge to cloud, and with a decentralized catalog and global access.
Save on IT costs because smart data processing at the edge, core, and cloud saves you time, tracks and protects your data, and increases the efficiency of your infrastructure.
Locate the most relevant data in minutes, not days. Easily build smart pipelines to collect, organize, transform, track, and access just the right data, no matter where it is.
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
In the Autonomous world, Edge, Core, and Cloud form must form a single seamless end-to-end infrastructure, cooperating on data processing, data communications, and data storage.
Unattended services, even in mildly complex situations, require advanced AI models generated by Deep Learning. They require massive data with rich data formats from complex sensors.
Autonomy means continuous improvement, ability to learn new objectives, respond to new scope, or manage new services. This generates a continuous data flow for continuous learning and continuous feedback from deployments.
Numerous, data-rich sensors in autonomous vehicles generate data at the alarming rate of 4-8 terabytes per hour, or even more in some cases. Akridata gives organizations the ability to intelligently ingest, transfer, store, and immediately access relevant data at massive scale — regardless of where the data resides at that moment, while also keeping up with the constantly changing nature of data relevancy.
One of the most promising areas of health innovation is the application of AI in medical imaging for uses like diagnosis, annotation, or redaction. The accuracy of AI models directly impact patient safety and adoption of AI solutions by radiologists. Akridata enables builders of these AI models to find the right data, access it securely, and iterate quickly to improve their models.
Making sense of the growing volumes of data ranging from online customer behaviors to intelligent retail locations create new challenges for retailers looking to optimize operations and enhance the customer experience. Akridata automates the manual, time consuming data management tasks so data scientists can immediately focus on the 1%-10% of data that is of value.
Whether it’s security surveillance or traffic management, maintaining citizens’ safety and privacy is a critical challenge for urban leaders. Not only does Akridata allow data teams to quickly focus in on the most relevant data, it also significantly reduces the time and cost required to transfer, store, and access data.
Founder/CEO, Virident (acq. WD)
Founder, VxTel (acq. Intel)
75 patents, 10 papers
PhD UIUC, IIT Madras
Founder/CTO, Virident (acq. WD)
Prof. Computer Science, NYU
90+ patents, 75 papers
PhD UIUC, IIT Kanpur
Founder, Trupeco (analytics)
Director, Mitsui Investments
Bear Stearns, Engineering, Level One (acq. Intel)
VP Strategic Alliances, Virident (acq. WD)
VP BD, Virsec
Founder, CoWare (acq. Synopsys)
IIT Varanasi, U. of Antwerp (Belgium)