HeyJaredSnacks Weekly on Data Science
This podcast is about making data science and machine learning knowledge accessible and less intimidating. Every week, I will handpick one selected industrial tech blog to break it down.
- Indexed episodes, last 90 days
- 13
- Latest publication
- Sep 28, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 6, 2026
Latest episodes
Measuring AI Infrastructure Impact with Causal Inference [Meta] (opens the original)
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In this episode, we discuss how Meta measures the business value of shared ML infrastructure when that value appears indirectly through downstream model launches. Instead of relying on adoption, usage, or traditional infrastructure metrics, the team built an observational causal framework around individual model launches. By comparing feasible adopters with comparable non-adopters and controlling for major confounding factors, the team estimated the incremental revenue associated with infrastruc
Building Taxonomies from Unstructured Text Using LLMs [Microsoft] (opens the original)
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In this episode, we discuss two LLM-assisted pipelines for turning unstructured text into a structured taxonomy with minimal manual annotation. The bottom-up approach lets semantic representations and clustering discover categories from the data, making it useful for open-ended exploration and relatively efficient at scale. The top-down approach starts with LLM-generated categories and recursively classifies and subdivides the corpus, making it more controllable and easier to align with business
Building Semantic IDs for Product Understanding at Scale [Instacart] (opens the original)
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In this episode, we talk about how Instacart identified a limitation of a standard taxonomy: it could classify products, but it couldn't connect them the way real shoppers do. The team developed a solution around Semantic IDs, which compress product embeddings into hierarchical discrete codes, while contrastive learning and taxonomy-based supervision make those codes more semantically meaningful. The result is a representation that can support better product understanding, catalog quality, and r
Agent-driven ML Exploration [Faire] (opens the original)
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In this episode, we discuss how Faire addresses a fundamental bottleneck in machine learning development: there are more potential experiments than human scientists can realistically run. They developed Autoscience, giving an AI agent a structured environment where it can plan, execute, evaluate, remember, and continuously refine experiments. What makes the approach effective is not the agent alone, but the combination of a reliable experimentation harness, integration with the existing ML stack
Building food metadata with LLM juries [DoorDash] (opens the original)
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In this episode, we discuss how DoorDash built a reliable metadata generation platform that could operate across millions of constantly changing menu items. The solution combined multimodal generation, automated evaluation through LLM juries, optimization loops for improving model behavior, and distributed infrastructure for cost-efficient scaling. By transforming unstructured menu information into precise, structured attributes, DoorDash unlocked many new product capabilities that ultimately he
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