Real-time artificial intelligence for everyone
By Chet Kapoor, Chairman & CEO of DataStax
Every business needs an artificial intelligence strategy, and the market has been validating this for years. Gartner® predicts that, “By 2027, over 90% of new software applications that are developed in the business will contain ML models or services, as enterprises utilize the massive amounts of data available to the business.1” And with the rise of tools like ChatGPT, more organizations than ever are thinking about how AI and ML can transform their business.
Still, most companies have not yet benefited from real-time AI. They fail because data is served too slowly in complicated environments, making real-time actions almost impossible. AI cannot work with the wrong data, at the wrong time, delivered by the wrong infrastructure.
So, how do leading enterprises use AI to drive business outcomes? And why should you care about real-time AI? Let’s dive in.
Winning with AI: It starts with data
A successful AI strategy starts with data. More specifically, you need real-time data at scale. Leaders like Google, Netflix, and Uber have already mastered this. Their ML models are embedded in their applications and use the same real-time data. They aggregate events and actions in real-time through streaming services, and expose this data to ML models. And they build it all on a database that can store massive volumes of event data.
Ultimately, it’s about acting on your data in the moment and serving millions of customers in real-time. Think about these examples:
● Netflix tracks every user’s actions to refine its recommendation engine, then it uses this data to propose the content you will love most
● Uber gathers driver, rider, and partner data to update a prediction engine that informs customers about wait times, or suggests routes to drivers
● FedEx aggregates billions of package events to optimize operations and share visibility with its customers on delivery status
How DataStax helps: A new class of apps
We have been working on unlocking real-time data for a long time at DataStax. We started with Apache Cassandra® 12 years ago, serving the largest datasets in the world. Then we made it a database-as-a-service with Astra DB and added Astra Streaming to make real-time data a reality.
Now, we have another exciting piece of the puzzle: Kaskada, a machine-learning company that recently joined forces with DataStax. Their feature engine helps customers get more value from real-time data. By adding Kaskada’s technology, we’ll be able to provide a simple, end-to-end stack that brings ML to data—not the other way around.
This unlocks a whole new class of applications that can deliver the instantaneous, personalized experiences customers demand – all in one unified open-source stack. Take the conversational AI company Uniphore, for example. Uniphore has an AI assistant that does sentiment analysis on sales calls. It helps sellers build better customer relationships and loyalty. Without the ability to process data in real-time, their solution would not be possible. Uniphore relies on DataStax to power its AI experience – with speed, scale, and affordability.
The future is bright
We believe every company should be able to deploy real-time AI at 3X the scale and half the cost. Our new mandate is clear: Real-time AI for everyone. We have the right data, at the right time, and the right infrastructure.
Now, it’s about executing the vision with our customers, communities, and partners. I’m super excited about making this a reality.
Click here to learn more about the power of real-time AI.
[1] Gartner, “A Mandate for MLOps, ModelOps and DeOps Coordination,” Van Baker, Nov. 22, 2022
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission.
About Chet Kapoor:
Chet is Chairman and CEO of DataStax. He is a proven leader and innovator in the tech industry with more than 20 years in leadership at innovative software and cloud companies, including Google, IBM, BEA Systems, WebMethods, and NeXT. As Chairman and CEO of Apigee, he led company-wide initiatives to build Apigee into a leading technology provider for digital business. Google (Apigee) is the cross-cloud API management platform that operates in a multi- and hybrid-cloud world. Chet successfully took Apigee public before the company was acquired by Google in 2016. Chet earned his B.S. in engineering from Arizona State University.
