A better database for
Machine Learning Ops

Use a streaming database with strong consistency to solve data latency, quality, and monitoring challenges faced by operating ML at scale.

Train and serve machine learning models on streaming datasets.

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.

What can you build with Materialize?

Unified feature training and serving

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.

Real-time online feature store

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.

Operate on multiple data sources

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.

Strict serializability

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.

Use an engine purpose built for real-time analytics

Materialize is built from the ground up to solve complex issues hindering adoption of streaming tools.

“Building an ML pipeline requires stitching multiple systems together”

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.

“We need to train our machine learning models faster”

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.

“Machine learning operations requires hiring for a specialized set of skills”

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.

“We can manage our feature store with our data warehouse”

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.

“We need to train our models with multiple data inputs and can’t move to streaming”

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.

“Moving to real-time training will results in errors from eventual consistency”

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.

How Maqqie Built a Real-Time Application as an Early-Stage Company
Featured Customer Story

Materialize has consistency guarantees; it’s correct, and not just eventually consistent. The alternatives simply don’t support consistency, and you end up wasting a lot of time implementing and troubleshooting.

Johan Stuyts
Johan StuytsData Architect and Backend Developer, Maqqie
Read Customer Story

Key Features

Incremental Materialized Views

Incremental Materialized Views

The power of materialized views - but always up-to-date

Easily Connect Kafka

Easily Connect Kafka

Easily manage streams from Kafka or Redpanda

Make Postgres Real-Time

Make Postgres Real-Time

Connect directly to any Postgres database via CDC.

dbt Integration

dbt Integration

Use dbt to model data and create real-time analytics

Full SQL - including joins

Full SQL - including joins

Full support for joins, subqueries, CTEs, inserts, and deletes.

Presents as Postgres

Presents as Postgres

Connect to the ecosystem of Postgres tools

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