# MLOps Week 31: Bridging Software Engineering and MLOps with Paul lusztin of Decoding ML

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Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2024-08-01T18:54:39+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/1594826
Audio file: https://content.rss.com/episodes/132586/1594826/mlops-weekly/2024_08_01_18_54_05_f7e9f5e1-27eb-4d95-ba6e-f2afa0590a20.mp3
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Duration seconds: 1978

## Resource

Transitioning from software engineering to MLOps requires shifting from an API-centric mindset to a data-centric one. This discussion explores how engineering principles apply to the non-deterministic challenges of LLM evaluation and pipeline architecture.

## Highlights
- Main idea: MLOps requires a fundamental shift from focusing on application layers to prioritizing data and model lineage
- Practical takeaway: Decouple feature, training, and inference pipelines to build scalable and reproducible platforms
- Failure mode: Building custom MLOps infrastructure from scratch often yields low ROI compared to using established, industry-standard tools
- Technical insight: Using a more powerful 'teacher' model to evaluate a smaller 'student' model is a viable strategy for managing LLM uncertainty
- Core challenge: The hardest problems in LLMOps remain centered on data quality and the lack of reliable, deterministic evaluation frameworks

## Topics

MLOps, LLMOps, Machine Learning Engineering, Model Evaluation, Vector Databases, Data-Centric AI, Software Engineering, Inference Pipelines

## Chapters
- 1:00 — The Journey to MLOps: A look at the transition from software engineering and deep learning research into the specialized field of MLOps.
- 6:00 — The Data-Centric Shift: How moving into MLOps changes your focus from APIs and UI layers to the primacy of data and models.
- 11:00 — Architecting Pipelines: The importance of decoupling feature processing, training, and inference for real-time systems.
- 15:30 — Build vs. Buy in MLOps: Why the ROI of building custom ML infrastructure has decreased as standardized tools mature.
- 18:05 — The Evaluation Crisis: Discussing the risks of using LLMs to evaluate themselves and the necessity of high-quality 'spoiler' models.
- 25:25 — LLMOps and Vector Databases: Analyzing how LLMs extend traditional data patterns through embeddings and vector search.
- 30:10 — The ML Engineer's Advantage: Why engineers experienced with black-box models are uniquely positioned to handle non-deterministic APIs.

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## Transcript

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