Data science

Why do 4 out of 5 ML models never make it to production?

Data science is business-critical — but too often, you must rely on data engineers to provide access to data, or deploy your ML models. Quix provides you with independence. A simple platform for you to build real time data pipelines, both for your ETL and model deployment.

Person looking in two laptop screens.

What can you build with Quix?

Explore streaming data

Visualize your data — as it’s streaming in, in an instant, and at any level of granularity. Explore real-time and historical data, and unify various sources in contextual streams. Which variables influence your models? What happens if you make a change? Find out by exploring within Quix or in Jupyter Notebooks.

Feature engineering

Develop and deploy your own data features without support from a data engineer. Use your favorite Python libraries to develop feature variables. Deploy them to production with a single click. Feed your models, dashboards and monitoring tools in real time, without developer support.

ML engineering

Train any model, anywhere. Export training data to any environment with a few clicks. Easily deploy it to Quix to process live data. Build real-time processing pipelines by combining feature creation and model predictions.

Test and iterate

Run your ML artifact in the Quix development environment — crafted specifically for Python professionals so there are no language barriers. Back test your results in real time against historical or live data streams. You can also A/B test models in parallel to uncover new insights and optimize.

Learn online

Online learning models re-train themselves in real time as new data emerges, adapting quickly to changing environments. Tiktok and Netflix use online learning, and now you can too. Simply combine an online learning library such as River with Quix to create next-generation adaptive ML products.

Person looking in two laptop screens.

Changing data science for good

Quix was founded by Formula 1 McLaren engineers who used stream processing to optimize race performance. They built the infrastructure to power predictions, evaluate scenarios, and distill millions of variables into concrete, actionable recommendations.

Their vision was to bring this stream processing technology to data scientists in any industry — without data engineers gatekeeping access to data and production viability.

With a Python-friendly platform purpose built for data scientists, Quix empowers you to do your best work.

Graphic showing Quix Streams windowing code

Introducing Streaming DataFrames

Learn how Streaming DataFrames can simplify real-time data processing in Python with a familiar DataFrame approach.
Graphic showing Quix Streams windowing code
Words by
Tomáš Neubauer
Featured image for the "Navigating stateful stream processing" post published on the Quix blog
Industry insights

Navigating stateful stream processing

Discover what sets stateful stream processing apart from stateless processing and read about its related concepts, challenges and use cases.
Featured image for the "Navigating stateful stream processing" post published on the Quix blog
Words by
Tim Sawicki
windowing in stream processing
Industry insights

A guide to windowing in stream processing

Explore streaming windows (including tumbling, sliding and hopping windows) and learn about windowing benefits, use cases and technologies.
windowing in stream processing
Words by
Daniil Gusev

Why the data pipeline is changing everything

Find out why analysts and market-watchers agree that traditional data processing must be replaced by stream processing. It’s happening now as innovators embrace new opportunities for greater personalization, automation and revenue.

Fill out the form to receive the white paper to your inbox.

Quix Report cover.