About us

Our Mission

At KappaML, we're on a mission to democratize online machine learning, making it accessible, efficient, and practical for all developers. We believe that as the world becomes increasingly data-driven, the ability to learn from streaming data in real-time will be crucial for creating intelligent and responsive applications.

Why KappaML?

The name KappaML draws inspiration from three fascinating connections:

  1. In the world of big data architectures, the Kappa architecture represents an evolution beyond the traditional Lambda architecture. While Lambda uses separate paths for batch and stream processing, Kappa architecture advocates for a pure streaming approach with a single code base. This aligns perfectly with our vision of simplifying machine learning on streaming data.

  2. In statistics and machine learning, Cohen's kappa coefficient (κ) is a robust measure of inter-rater reliability that accounts for agreement occurring by chance. This statistical foundation reflects our commitment to building reliable and accurate machine learning systems that can effectively learn from streaming data.

  3. In Japanese folklore, the Kappa is a water deity that dwells in rivers and streams. This connection to flowing water mirrors our focus on streaming data, and it's no coincidence that we've built our platform on top of River, the leading Python library for online machine learning. Just as the mythical Kappa is inseparable from its flowing habitat, KappaML is intrinsically linked to the continuous flow of streaming data.

Streaming Data is Everywhere

In today's digital landscape, data never stops flowing. From user interactions on websites to IoT sensor readings, from financial transactions to social media feeds - streaming data is ubiquitous. Traditional batch-oriented machine learning approaches fail to capture the dynamic nature of these continuous data streams, often leading to stale models and missed opportunities.

We're building tools that enable developers to harness the power of streaming data, allowing models to adapt and learn continuously as new information arrives. This real-time learning capability is essential for applications that need to respond to changing patterns, detect anomalies as they happen, or personalize experiences in the moment.

Democratizing Online Machine Learning

Online machine learning has historically been complex to implement and difficult to scale. Advanced algorithms, resource constraints, and the lack of developer-friendly tools have kept this powerful approach out of reach for many.

KappaML is changing that by:

  • Creating intuitive APIs that abstract away complexity
  • Developing efficient algorithms that work on resource-constrained environments
  • Building open-source tools that make online learning accessible to all developers
  • Providing comprehensive documentation and learning resources

We believe that online machine learning shouldn't require an entire system architecture study to implement effectively. Our goal is to make these techniques as approachable as traditional machine learning has become. Maybe even more.

Cutting-edge AutoML for Streaming Data

The field of Automated Machine Learning (AutoML) has revolutionized how models are built for static datasets, but automated approaches for streaming data remain largely unexplored territory.

We're bridging this gap by:

  • Bringing cutting-edge online AutoML research into practical, production-ready tools
  • Developing algorithms that automatically adapt to changing data distributions
  • Creating systems that continuously optimize model hyperparameters
  • Building solutions that intelligently manage computational resources for streaming workloads

By combining the best of AutoML with online learning techniques, we're creating a new generation of tools that empower developers to build intelligent, adaptive systems with minimal manual tuning.

Open Source Projects

Check out our GitHub Organisation.

Careers

We do not have have a list of job descriptions at the moment, but you can send us an open application at [email protected].