# MLOps Week 19: Navigating Traditional ML and LLM Workflows with Piero Molino

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Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-19-navigating-traditional-ml-and-llm-workflows-with-piero-molino.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2023-06-06T04:15:48+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/982249
Audio file: https://content.rss.com/episodes/132586/982249/mlops-weekly/2023_06_06_04_13_30_f5407fe1-1b1a-4dc6-8881-9b9919192905.mp3
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Duration seconds: 1838

## Resource

Piero Molino explains how declarative machine learning frameworks like Ludwig reduce complexity by abstracting away model architecture. The discussion explores the shift from manual pipeline construction to integrated platforms that manage the entire ML lifecycle.

## Highlights
- Main idea: Declarative configuration, similar to Terraform, allows users to define ML inputs and outputs without manual architecture design
- Practical takeaway: Using abstraction layers like Ludwig saves significant engineering time for both experienced data scientists and non-experts
- Failure mode: Relying solely on prompting (natural language) lacks the deterministic mental model and precision provided by programming languages
- Industry trend: The MLOps landscape is moving from fragmented 'best-in-class' tool stitching toward cohesive, high-level integrated platforms
- Future outlook: The emergence of specialized query languages like LMQL suggests a return to structured, SQL-like interfaces for controlling LLMs

## Topics

MLOps, LLM Workflows, Declarative Machine Learning, Ludwig, Predibase, AutoML, Machine Learning Infrastructure, Prompt Engineering

## Chapters
- 1:00 — From Research to Uber AI: Piero discusses his transition from PhD research in question answering to building foundational ML tools at Uber.
- 3:20 — The Power of Declarative ML: An introduction to the core abstraction of Predibase and how declarative configurations simplify model building.
- 5:45 — Automating Model Configuration: How specifying simple input/output types in Ludwig allows the system to automatically select and configure optimal models.
- 8:10 — Ludwig vs. AutoML: Distinguishing between high-level automation frameworks and the more flexible, developer-centric approach of Ludwig.
- 10:10 — Targeting the ML User Base: Analyzing how abstraction tools serve both expert data scientists looking for speed and new users entering the field.
- 14:50 — The Shift Toward Integrated Platforms: Why the industry is moving away from stitching together disparate tools toward unified MLOps platforms.
- 21:30 — The Limits of LLM Prompting: A critique of natural language prompting and the potential for structured, SQL-like languages to govern LLM workflows.

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## Transcript

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