Data Infrastructure for Machine Learning
Learn how modern data engineering powers machine learning in production. Discover how raw tables become reliable feature pipelines, explore lakehouse architectures, and build automated MLOps workflows that keep models accurate over time.
Like Duolingo, but for Data Infrastructure for Machine Learning. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
A deep dive: 47 levels across 5 sections, about 94 minutes end to end, roughly 19 days at five minutes a day. It moves through Feeding the Model: From Raw Tables to Usable Features, Structuring the Flow: The Core Data Layers, Pipeline Reliability: Orchestrating Clean Data, MLOps in Production: The Operational Feedback Loop, and Under the Hood: Compute and Storage Engines. It starts from scratch.
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Key ideas in Data Infrastructure for Machine Learning
- Machine learning models expect numeric inputs and clean types, not raw strings or unhandled missing values
- Extreme differences in scale, such as small counts versus multimillion-dollar figures, distort how algorithms weigh inputs
- Raw operational databases prioritize fast transactional writes, not clean, standardized arrays for algorithms
- A pipeline first pulls data out of upstream operational stores without modifying the source
- Transformation cleans types and reshapes raw values into model-ready numbers
- Automated validation checks data ranges and formats before delivering records to the destination storage
- Raw columns record individual transactional facts exactly as they happen in an app or store
- Features are computed numerical signals, such as rolling averages or distances, designed to reveal patterns to a model
- Models learn patterns from mathematical summaries of behavior rather than raw log entries
- Models rely on feature values having the exact same mathematical distributions in production as in training
- A discrepancy in transformation logic creates silent failures where code runs without throwing errors but produces degraded predictions
- Shared pipeline definitions ensure consistent transformation rules across both training datasets and live serving infrastructure
- An immutable source ensures past raw data cannot be silently modified or overwritten
- Deterministic transformations ensure that re-running the pipeline on identical input always produces identical features
- A validated destination guarantees corrupted or malformed data is stopped before the model consumes it
- A feature store serves as a central catalog of ready-to-use metrics and attributes for machine learning
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Data Infrastructure for Machine Learning the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
What happens if you feed a model both a small count and a multimillion-dollar figure without balancing them?
Get it right to open this lesson and 46 more in the app.
Where Data Infrastructure for Machine Learning takes you
- 1
Feeding the Model: From Raw Tables to Usable Features
- The First Machine Learning Pipeline
- Feature Stores and Shared Data Assets
- Preventing Data Leakage and Skew
- 2
Structuring the Flow: The Core Data Layers
- The Medallion Architecture
- Data Lakes, Warehouses, and Lakehouses
- Batch and Streaming Data Pipelines
- 3
Pipeline Reliability: Orchestrating Clean Data
- Workflow Orchestration and DAGs
- Data Quality, Testing, and Contracts
- Lineage, Metadata, and Governance
- 4
MLOps in Production: The Operational Feedback Loop
- Model Registries and Artifact Versioning
- Monitoring Data Drift and Concept Drift
- Continuous Retraining and Safe Deployment
- 5
Under the Hood: Compute and Storage Engines
- Distributed Compute for Heavy Pipelines
- Storage Formats and Query Optimization
5 sections · 14 units · 47 levels. Built to play, not to enroll.
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Add Tomo to ChatGPT- What to ask
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You pick the voice
Data Infrastructure for Machine Learning is taught in the Bestie style: your friend who just gets it. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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