Use a streaming database with strong consistency to solve data latency, quality, and monitoring challenges faced by operating ML at scale.
According to industry reports, only 22 percent of companies using machine learning have successfully deployed a model. And out of that cohort, over half believe deploying another would take at least 90 days. Often, the challenge is not training the model but getting up-to-date, correct information for it to score.
Materialize has all the capabilities necessary to deliver a feature store that continuously updates dimensions as new data becomes available without compromising on correctness or speed. And because Materialize is Postgres wire compatible, the feature can be served or queried using your favorite Postgres driver. No custom integrations are required.
The most common frameworks for machine learning require separate systems for feature training and feature serving. Materialize is a database wrapped around a stream processor - enabling teams to train and serve features with a single solution.
Use Materialize to complement your offline feature store, which is built primarily to store and access historical feature data. Build real-time predictions with millisecond latency reads and high throughput writes with Materialize.
Materialize supports cross-stream and multi-way joins, without the need to microbatch or round-trip data at high latencies. Use the same existing SQL to train ML models in batch - but instead adapt models in real-time.
Materialize makes it simple to build a real-time feature store without sacrificing correctness. With strict serializability, you don’t need to give up correctness guarantees to train ML models with multiple data inputs.
Materialize is built from the ground up to solve complex issues hindering adoption of streaming tools.
Current models for machine learning operations put a ton of burden on the user - managing separate systems for raw data collection, feature storage, processing, and consumption. Materialize manages all of those pieces in a single streaming database.
Data warehouses power many machine learning use cases - but can only work in batches. Materialize incrementally maintains the results of SQL queries in real-time so machine learning models never train off of old data.
Hard-coded logic requires a ton of effort to update and maintain as business requirements shift. Materialize allows you to adjust and test using standard SQL, saving time both in the short and long term.
Data warehouses are helpful for storing historical features as an offline feature store. With Materialize, models can be trained and served in real-time as an online feature store - and historical models can be enriched by sinking already-generated features into your data warehouse.
Materialize supports cross-stream and multi-way joins, without the need to microbatch or round-trip data at high latencies. Focus on what you want to build, and Materialize will handle how to get it.
Don’t give up correctness guarantees for speed. All results from Materialize reflect correct answers, and models should never be falsely impacted by late-arriving labels.
The power of materialized views - but always up-to-date
Easily manage streams from Kafka or Redpanda
Connect directly to any Postgres database via CDC.
Use dbt to model data and create real-time analytics
Full support for joins, subqueries, CTEs, inserts, and deletes.
Connect to the ecosystem of Postgres tools
Get updated results as data changes with query subscriptions.
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