Time Series Data and Real World AI: A Fireside Chat

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Recently, InfluxData CEO Evan Kaplan sat down with Developer Advocate Jay Clifford to discuss the role of time series data and AI in industry, how it’s evolving, and specifically, the role of time series data in AI. They also discussed the future of InfluxDB in terms of real-time analytics and its role in the AI landscape.

Main Topics Covered

  • The current state of AI in industry and its future

  • The role of time series data in AI

  • The impact of AI on InfluxDB

  • The future of using InfluxDB for real-time analytics


The role and importance of AI in the time series data space

The discussion stressed the vital role of AI in the time series data space. Kaplan emphasized the significance of AI, stating that all software will incorporate AI going forward, making it a necessity rather than an option. He expressed a particular interest in “real world AI,” which makes things in the physical world smarter, more effective, and more autonomous.

“A lot of what we’re building is like pickaxes and shovels during the Gold Rush,” Kaplan suggested. He further explained, “We’re foundational. We don’t necessarily develop AI models ourselves. The experts in specific fields who understand them, who know how to build those models, they do that work.”

In short, InfluxDB doesn’t provide the AI but provides crucial infrastructure to those who do.

The integration of AI and time series data

Another salient topic highlighted was the integration of AI and time series data. Kaplan emphasized that, in the future, real-world AI applications like self-driving cars or robotics will depend heavily on time series data, and companies cannot afford to ignore this.

“All software is AI going forward,” he said. He went on to explain how this applies to InfluxDB and its role in AI and time series data: “What time series is in that equation is instrumentation. What you’re trying to do with AI, right, is to say, ‘what happened, what happened, what happened, what’s going to happen, what could happen, what are the probabilities of things happening?’”

Time series data is critical for understanding past events to predict future outcomes, a critical aspect of AI functionality.

The future of InfluxDB in the context of AI and real-time analytics

Kaplan outlined the future direction of InfluxDB in the context of AI and real-time analytics. He stated that InfluxDB aims to provide the foundational layer for AI solutions, enabling users to build their AI models based on the data stored within InfluxDB.

“I have a pretty clear view of where we fit and what we’re building for,” Kaplan stated. He further clarified, “It may not be the exciting headline role, but it’s a role we feel really comfortable executing against.”

Key insights

  • AI is becoming an essential part of all software going forward and cannot be ignored.
  • Time series data is fundamental to AI—it helps us understand what happened and predict what could happen.
  • InfluxDB is a foundational tool that stores and manages the necessary real-time data for creating AI models.
  • InfluxDB is about both real-time data and storing historical data for building AI and ML models.
  • InfluxDB 3.0 aims to be more efficient across large sets of data, which is crucial for real-time analytics.
  • Real-time analytics are central to understanding AI and building sophisticated control systems.

Key quotes

  • “All software is AI going forward and that is not something you can ignore.”

  • “We’re really providing that foundational bedrock layer for people to have real-time data.”

  • “Our view is that the real-time analytic stuff is core to really understanding AI, to building foundational models, and to understanding those dynamics.”

  • “The fact that we have the ability to ingest and work with data in real-time feeds these models and allows us to make real-time decisions in that area.”

Watch Evan and Jay’s discussion:

To see how to use InfluxDB with the AI tool Quix, check out this blog post.