1) Motivations; 2) MLOps Foundations; 3.1) The Past - 2015 - Genesis; 3.2) The Past - 2018 - Messy Innovation; 3.3) The Past - 2023 - LLMOps; 4) The Future - 2025-2035 Outlook; 5) Reflections.
The lifecycle of a machine learning model only begins once it’s in production. In this talk we take a practical journey through the last decade of production ML, tracing back the early beginnings of MLOps to the respective research and projects that helped drive the movement forward. We cover how the ecosystem went through explosive growth through COVID with a broad range of tools and vendors tacking similar problems in very different ways. We then talk about the most recent trends in LLMOps which has shifted the stack from training-centric to inference-centric as pre-trained models have become broadly available. Namely on how the locus of engineering moves to the application layer (ie inference time), introducing new artifacts such as prompts, vector databases, and tool metadata, and accelerating another wave of ecosystem heterogeneity.
With those lessons in place, we look forward to 2035 through a set of pragmatic milestones for consolidation and standardization: how monitoring and observability become more ubiquitous, how MLOps and LLMOps stacks align, how time-to-production compresses, and how operations gradually evolves toward more autonomous patterns (progressive rollouts, agent-assisted RCA, and early self-healing behaviors).
Finally, we close with actionable guidance grounded in production reality: how to right-size platform complexity to organizational scale, where to invest early to reduce future operational debt, and how to increase the scale of ML delivery while actively reducing system complexity. Attendees should leave with a coherent mental model of the MLOps landscape, a sharper understanding of why production ML remains hard, and a concrete set of engineering priorities for building reliable ML systems through the next decade.
