Beyond Build vs. Buy: Engineering ML Pipelines that Scale

Modern ML pipelines often start simple—but they rarely stay that way. As use cases grow more complex and real-time demands increase, many teams find their homegrown systems buckling under the weight of scale, cost, and maintenance.

In this fireside chat, Tecton CPO Scott Castle and Product Manager Nick Joung unpack the hidden costs of DIY feature pipelines, and the engineering practices that separate short-term scrappiness from long-term scalability.

They’ll share patterns from dozens of ML platform teams and offer practical guidance on how to recognize when it’s time to rethink your stack.

You’ll learn how to:

• Spot early signs of pipeline fragility, cost bloat, and technical debt

• Recognize when your stack needs versioning, CI/CD, and SDLC discipline

• Understand the risks introduced by real-time and streaming workloads

• Evaluate whether your current architecture can support what’s next

Whether you’re supporting fraud detection, personalization, or LLMs, this session will help you get ahead of the bottlenecks—before they slow you down.