# MLOps Week 7: Scaling AutoML with Nirman Dave

Page: https://stenobird.com/podcast/mlops-weekly/mlops-week-7-scaling-automl-with-nirman-dave
Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-7-scaling-automl-with-nirman-dave.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2022-07-26T20:26:44+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/564607
Audio file: https://content.rss.com/episodes/132586/564607/mlops-weekly/20220726_080739_903604b2671a25d15721fb3a4f959d81.mp3
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Duration seconds: 1394

## Resource

AutoML acts as a 'calculator' for data analysts, automating the heavy lifting of model training while leaving feature engineering to human experts. This episode explores how no-code tools bridge the talent gap by allowing domain experts to deploy predictive models without deep machine learning expertise.

## Highlights
- Main idea: AutoML is not a replacement for data scientists, but a productivity multiplier similar to how a calculator assists accountants
- Practical takeaway: The real value in modern ML lies in feature engineering and selecting the right data, rather than manual algorithm tuning
- Failure mode: Relying solely on automated tools without injecting domain knowledge regarding data quality and feature relevance
- Strategic insight: Focusing on supervised learning for tabular data provides the highest immediate business value for most organizations
- Operational shift: The role of the analyst is moving from 'nitty-gritty' coding to high-level decision-making regarding data inputs and model evaluation

## Topics

AutoML, Machine Learning Operations, No-code AI, Feature Engineering, Predictive Analytics, Data Science Workflow, Supervised Learning, Tabular Data

## Chapters
- 1:00 — Foundations of Neural Networks: Nirman discusses his experience building neural networks from scratch using NumPy and how it shaped his understanding of ML.
- 2:40 — The Talent Gap in Data Science: Reflecting on his time at Streamlabs, Nirman describes the massive demand for predictive modeling versus the scarcity of specialized talent.
- 4:25 — AutoML as a Calculator: An analogy comparing AutoML to accounting calculators, emphasizing how it accelerates work rather than replacing the professional.
- 6:10 — Empowering Entry-Level Analysts: How no-code tools provide analysts with the 'superpowers' of a PhD engineer to execute complex tasks quickly.
- 9:30 — The Speed of Automated Training: A look at the technical backend that allows for building highly accurate models in under a minute using automated hyperparameter tuning.
- 11:10 — Engineering for Production: How the Obviously AI team builds models designed specifically to be productionized for end-users using standard stacks like Spark and TensorFlow.
- 19:35 — The Importance of Feature Engineering: Discussing how the analyst's primary responsibility is now defining the right data features and managing organizational data silos.

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## Transcript

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