This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack.
Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer — systems thinking is.

We'll use concrete examples to walk through common failure modes in ML pipelines, highlight where analogies fall apart, and show how to build systems that tolerate failure, adapt to change, and support iteration without regressions.

Topics covered include:
- Common failure modes in ML pipelines
- Modular design: feature, training, inference
- Built-in observability, versioning, reuse
- Orchestration across batch, real-time, LLMs
- Platform-agnostic patterns that scale

Key takeaways:
- Resilience > diagrams
- Separate concerns, embrace change
- Metadata is your backbone
- Infra should support iteration, not block it