Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Rockset Redefines the Data Stack for AI-Powered Computer Vision

Leading Real-Time Indexing Database Enables Rapid Application Development Using Computer Vision Data 

Rockset, the real-time indexing database in the cloud, announced that it is redefining the real-time data stack for AI-powered computer vision, an industry that is advancing what’s possible in healthcare, gaming, logistics and retail. For example, one of Rockset’s customers is AI-powered autonomous checkout provider Standard Cognition, which recently reopened its San Francisco convenience store, bringing a grab-and-go experience to brick-and-mortar shopping. Rockset takes an entirely new approach to ingesting, analyzing and serving computer vision data so developers can iterate faster using the latest data, without sacrificing production performance.

In addition to algorithmic challenges that arise with running AI-powered computer vision systems, developers must also contend with increasing volumes of complex, fast-moving data from a multitude of sources. On top of this, developers need the ability to easily and quickly build and customize models based on production outcomes to ensure accuracy. Considerable effort must go into complex extract-transform-load (ETL) and data pipelines, which are difficult to build, expensive to maintain and introduce hours of latency. This bottleneck makes operating on real-time data and rapid iteration next to impossible—both requirements for success in today’s AI-driven world.

Recommended AI News: Asurion Launches New Data Science Scholarship Program At Fisk University With $200,000 Investment

Intelligent applications demand fast answers on fresh data. Rockset is a real-time indexing database in the cloud, that automatically builds indexes that are optimized not just for search but also aggregations and joins, making it fast and easy for applications to query data, regardless of where it comes from and what format it is in—a critical functionality when developing new features and verifying the accuracy of AI models.  Rockset handles data at different frequencies from different data sources, and makes that data easily accessible to developers for ad-hoc analysis, prototyping and moving new features into production.

Related Posts
1 of 40,453

“For AI applications, companies that can iterate really fast to constantly improve their product have a huge competitive advantage,” said Venkat Venkataramani, co-founder and CEO of Rockset. “This is why data architectures are moving from batch analytics to real-time, and developers are pushing the limits of what traditional online transactional processing databases and data warehouses can handle. Rockset is the only real-time indexing database built for the cloud, that affords developers the ability to create fast, massively scalable data APIs on fresh real-time data sets in a matter of minutes instead of weeks. This is the next phase of innovation that the AI market desperately needs.”

Recommended AI News: QuickStart Launches DataScienceAcademy.Io, Data Science Workforce Readiness And Certification Training Platform

AI-Powered Computer Vision Customers Embrace Real-Time Data for Rapid Development with Rockset

Rockset supports customers in building tomorrow’s intelligent applications. Using Rockset, developers can create fast data APIs using SQL to query structured, semi-structured, geo and time-series data in real time, so that adaptive and engaging applications can be built faster.

“The team at Standard Cognition is always looking to increase the accuracy of the computer vision platform and add new features to the product. We need to be able to drive product improvements from conception to production rapidly, and that involves being able to run experiments and analyze real-time metrics quickly and simply,” says Tushar Dadlani, computer vision engineering manager at Standard Cognition. “Using Rockset in our development environment gives us the ability to perform ad-hoc analysis without a significant investment in infrastructure and performance tuning. We have over two thirds of our technical team using Rockset for their work, helping us increase the speed and agility with which we operate.”

Recommended AI News: Zepl Integrates Snowflake 2.6 Spark Connector To Accelerate And Simplify Data Science At Scale

Comments are closed, but trackbacks and pingbacks are open.