# MLOps Week 28: Featureform's CEO Breaks Down "Real-Time" Machine Learning

Page: https://stenobird.com/podcast/mlops-weekly/mlops-week-28-featureform-s-ceo-breaks-down-real-time-machine-learning
Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-28-featureform-s-ceo-breaks-down-real-time-machine-learning.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2024-03-28T01:16:49+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/1410852
Audio file: https://content.rss.com/episodes/132586/1410852/mlops-weekly/2024_03_28_01_16_05_7fd9f175-e804-4df3-b54e-fad7e9635fb1.mp3
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Duration seconds: 1107

## Resource

Stop using 'real-time' as a vague buzzword and start defining it through latency, online serving, and feature freshness. This episode provides a framework for breaking down ML requirements into actionable engineering constraints.

## Highlights
- Main idea: Real-time ML is not a single metric but a combination of latency, online serving, and feature freshness
- Practical takeaway: Always define a 'latency budget' that accounts for the entire pipeline, including network and feature retrieval, not just model inference
- Failure mode: Over-engineering with streaming features when batch processing is sufficient, leading to unnecessary system complexity and higher costs
- Main idea: Feature freshness and latency exist in a trade-off; higher freshness typically requires more expensive, higher-latency infrastructure
- Practical takeaway: Use on-demand features to combine pre-computed batch/streaming data with request-time data for complex calculations like transaction percentages

## Topics

Machine Learning, MLOps, Real-time Systems, Feature Engineering, Data Latency, Streaming Data, Online Serving, System Architecture

## Chapters
- 1:00 — Defining the Three Pillars of Real-Time: An introduction to breaking down real-time ML into latency, online serving, and real-time features.
- 2:25 — The Latency Budget: How to evaluate acceptable latency based on use cases like fraud detection versus recommendation engines.
- 3:40 — Online Serving vs. Offline Models: Distinguishing between models that must be always-on and those that can run in a batch setting.
- 5:00 — The Stakes of Online Models: Understanding the operational importance and immediate impact of models running in an online environment.
- 7:35 — The Two Axes of Real-Time Features: Analyzing features through the dual lenses of latency (speed of retrieval) and freshness (data staleness).
- 8:55 — Optimizing Feature Windows: How choosing appropriate time windows for features can reduce compute costs and latency.
- 10:15 — Batch, Streaming, and On-Demand Features: A deep dive into the three feature types, their implementation complexities, and when to use each.
- 15:30 — The Complexity of Streaming: The engineering challenges and increased failure modes associated with streaming data pipelines.

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## Transcript

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