Real-Time Predictions with Machine Learning & Streaming Data Transforms

July 25, 2024 | 09:00 AM PDT (UTC-7)


Join us for a Complimentary Live Webinar Sponsored by Redpanda

In this session, we'll address how to simplify data structures in AI applications, emphasizing the importance of not overcomplicating data architecture when constructing stateless pipelines for real-time analytics.

We'll cover the creation of an efficient data platform using inline streaming data transforms powered by WebAssembly (WASM), tailored for dynamic industries (we will use food delivery as an example). We'll show how to simplify your data stack and demonstrate with a lab how complex data structures can hinder the agility and performance of AI systems. The lab will focus on stateless pipelines, where each data item is processed independently, and showcase how to build scalable and robust AI applications without the burden of cumbersome data frameworks. 

Attendees will see how a streaming data platform can support the seamless real-time data processing and instant transformations that are crucial to responsive and accurate AI-driven predictions. They will learn how to avoid common pitfalls associated with complex data structures and data stacks, and will gain insights into creating more effective, agile, and responsive applications – especially for AI. The methodologies are applicable across various industries and use cases.

We will cover:

  • Streamlined data ingestion and transformation
  • Real-time machine learning
  • Simplified infrastructure setup
Christina Lin

Developer Advocate, Redpanda


Christina has 20+ years of experience in software development. She has worked as a developer, consultant, architect. She is an advocate for making innovative solutions down to earth and making them easily accessible for everyone. Skilled in Open Source technology such as Redpanda, Apache Camel, Kafka and Kubernetes, Ansible.