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

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.

Topics covered include:

- Modular design: feature, training, inference

- Built-in observability, versioning, reuse

- Orchestration across batch, real-time, LLMs

- Platform-agnostic patterns that scale

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing ML Pipelines.

The only certainty in life is that the pendulum will always swing. Recently, the pendulum has been swinging towards repatriation. However, the infrastructure needed to build and operate AI systems using Python in a sovereign (even air-gapped) environment has changed since the shift towards the cloud. This talk will introduce the infrastructure you need to build and deploy Python applications for AI - from data processing, to model training and LLM fine-tuning at scale to inference at scale. We will focus on open-source infrastructure including: a Python library server (Pypi, Conda, etc) and avoiding supply chain attacks a container registry that works at scale a S3 storage layer a database server with a vector index

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing ML pipelines

In this talk, we will examine how to decompose AI systems into more manageable parts that then can be independently developed and tested, and then easily be composed together into an AI system. We will present a unified architecture for building batch, real-time, and LLM AI systems around 3 classes of machine learning pipelines: feature pipelines, training pipelines, and inference pipelines.

Just like you can make great music with 3 chords, we will show tens of examples of great AI systems built with our 3 ML pipelines (and the truth!).

We will show how our 3-pipeline architecture helps align teams and accelerates time to value and quality.