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.
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.
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.
From the blog
How to fix common issues when using Spark Structured Streaming with PySpark and Kafka
Quix Streams—a reliable Faust alternative for Python stream processing
How to enrich a stream of data in real time with Quix and Redis
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.
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