Materialize Features to an Online Store
Push features so they are ready at low latency.
Materialize Features to an Online Store is a free MLOps Academy lesson on CoddyKit — lesson 3 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.
Why Materialize
Your features sit in batch storage, but predictions need them in milliseconds. Materializing copies the latest values into a fast online store. ⚡
Offline Is Too Slow
The offline store is great for history but scanning Parquet at request time would be far too slow for live serving.
The Online Store
The online store is a key-value database, often Redis, that returns the newest feature for an entity almost instantly.
Latest Value Only
The online store keeps just the most recent value per entity, not the full timeline. It is optimized for now, not for the past.
Run Materialize
The feast materialize command moves feature values from the offline store into the online store for a time range.
feast materialize 2024-01-01T00:00:00 2024-01-02T00:00:00Incremental Updates
Use materialize-incremental to load only what is new since the last run, so you do not reprocess everything each time.
feast materialize-incremental $(date +%Y-%m-%dT%H:%M:%S)Keep It Fresh
Schedule materialization on a cron so the online store stays fresh. Stale features quietly hurt prediction quality.
Reading at Serve Time
At prediction time call get_online_features with the entity rows you care about, and Feast returns ready-to-use values.
store.get_online_features(features=["driver_stats:conv_rate"], entity_rows=[{"driver_id": 1001}])Turn It Into a Dict
Call to_dict on the result to get a plain Python dictionary you can feed straight into your model.
feature_vector = response.to_dict()Mind the TTL
If a feature is older than its ttl, Feast returns null online. That is your signal to materialize more often.
The Online Path
So the flow is: write features, materialize to the online store, then read them fast per request. That last hop is what makes real-time serving work. 🚀
Quick Check
Which command loads features into the online store?
Recap
You materialized features into a fast online store, kept them fresh with incremental loads, and read them at serve time with get_online_features. Next, you'll get the timing exactly right.
Frequently asked questions
Is the “Materialize Features to an Online Store” lesson free?
Yes — the full text of “Materialize Features to an Online Store” 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 “Materialize Features to an Online Store”?
Push features so they are ready at low latency. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Materialize Features to an Online Store” 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