Real-Time Machine Learning and Smarter AI with Data Streaming

Streaming Audio: Apache Kafka® & Real-Time Data

Are bad customer experiences really just data integration problems? Can real-time data streaming and machine learning be democratized in order to deliver a better customer experience? Airy, an open-source data-streaming platform, uses Apache Kafka® to help business teams deliver better results to their customers. In this episode, Airy CEO and co-founder Steffen Hoellinger explains how his company is expanding the reach of stream-processing tools and ideas beyond the world of programmers.
Airy originally built Conversational AI (chatbot) software and other customer support products for companies to engage with their customers in conversational interfaces. Asynchronous messaging created a large amount of traffic, so the company adopted Kafka to ingest and process all messages & events in real time.
In 2020, the co-founders decided to open source the technology, positioning Airy as an open source app framework for conversational teams at large enterprises to ingest and process conversational and customer data in real time. The decision was rooted in their belief that all bad customer experiences are really data integration problems, especially at large enterprises where data often is siloed and not accessible to machine learning models and human agents in real time.
(Who hasn’t had the experience of entering customer data into an automated system, only to have the same data requested eventually by a human agent?)
Airy is making data streaming universally accessible by supplying its clients with real-time data and offering integrations with standard business software. For engineering teams, Airy can reduce development time and increase the robustness of solutions they build.
Data is now the cornerstone of most successful businesses, and real-time use cases are becoming more and more important. Open-source app frameworks like Airy are poised to drive massive adoption of event streaming over the years to come, across companies of all sizes, and maybe, eventually, down to consumers.
EPISODE LINKS

• Learn how to deploy Airy Open Source - or sign up for an Airy Cloud test instance
• Google Case Study about Airy & TEDi, a 2,000 store retailer
• Become an Expert in Conversational Engineering
• Supercharging conversational AI with human agent feedback loops
• Integrating all Communication and Customer Data with Airy and Confluent
• How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka
• Real-Time Threat Detection Using Machine Learning and Apache Kafka
• Watch the video
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent
• Use PODCAST100 to get $100 of free Confluent Cloud usage (details)

챕터

1. Intro (00:00:00)

2. What is Airy? (00:04:48)

3. What is Airy's architecture? (00:11:49)

4. How does Airy work? (00:16:19)

5. Incorporating data mesh best practices (00:23:15)

6. What differentiates Airy from other stream-processing tools? (00:26:21)

7. Customer use-cases (00:31:18)

8. What stage is Airy in as a company? (00:33:18)

9. Getting started with Airy (00:36:04)

10. It's a wrap! (00:37:08)