Fetch Point-in-Time Correct Features
Avoid label leakage when building training sets.
Fetch Point-in-Time Correct Features is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Time-Travel Problem
To train fairly, each label needs the feature values that were known before the event, not after. Mixing up time leaks the future into your model. ⏳
What Label Leakage Is
Label leakage happens when a feature secretly carries information from after the prediction moment. The model looks brilliant offline, then flops live.
A Concrete Trap
Joining today's account balance onto a loan made last year is a classic leak. The right value is the balance as it was at loan time.
Point-in-Time Joins
A point-in-time join matches each event to the latest feature value that existed at or before that event's timestamp. No peeking ahead.
The Entity DataFrame
You hand Feast an entity dataframe: entity keys plus an event timestamp for each training row you want features for.
entity_df = pd.DataFrame({"driver_id": [1001], "event_timestamp": [pd.Timestamp("2024-01-01")]})Get Historical Features
Call get_historical_features with that dataframe, and Feast does the time-correct join against the offline store for you.
store.get_historical_features(entity_df=entity_df, features=["driver_stats:conv_rate"])Turn It Into a Dataset
Call to_df on the result to get a training dataframe where every row carries correct, leak-free feature values.
training_df = job.to_df()TTL Guards Freshness
If the nearest value is older than the feature's ttl, Feast leaves it null instead of attaching something out of date.
Same Logic, Two Stores
Historical reads hit the offline store for training; online reads hit the online store for serving. The feature definition stays identical, so there is no skew.
Why It Matters
Point-in-time correctness is what makes offline scores you can trust. Skip it and your validation numbers are a comforting lie. 🎯
The Big Picture
Define once, materialize for speed, and join by time for training. Together these give you a feature store that is fast, consistent, and honest.
Quick Check
Why use a point-in-time join when building a training set?
Recap
You used point-in-time joins via get_historical_features to build leak-free training sets, while the same definitions serve online. That is a complete feature store loop. 🏁
Frequently asked questions
Is the “Fetch Point-in-Time Correct Features” lesson free?
Yes — the full text of “Fetch Point-in-Time Correct Features” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Fetch Point-in-Time Correct Features”?
Avoid label leakage when building training sets. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Fetch Point-in-Time Correct Features” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Why Feature Stores Exist
- Define Feature Views with Feast
- Materialize Features to an Online Store
- Fetch Point-in-Time Correct Features