The Definitive Guide to Productionizing Data for AI

Getting AI Data to Production Doesn’t Have to be Hard

Engineering teams know the harsh reality: most AI projects never make it to production. The culprit isn't poor models or lack of data — it's the massive engineering challenge of delivering fresh, relevant context to AI in production.

This technical guide cuts through the complexity, showing you how to:

• Streamline feature production for data scientists and ML engineers
• Ensure reliable data consistency across offline/online environments
• Solve the critical challenges of serving context at sub-100ms latency
• Efficiently compute, store, and serve features, embeddings, and prompts

Whether you're dealing with fraud detection requiring real-time signals, recommendation engines needing fresh user context, or LLMs demanding richer knowledge bases, this framework gives you the architecture and best practices to make it work.

Stop wrestling with brittle data pipelines. Learn how leading engineering teams are building smarter AI systems that can actually handle production workloads.

Download now to get the complete technical playbook for context-aware AI that delivers real business value.