Matrix AI is a Sydney based company that provides end to end machine learning application development services. We deliver full stack software solutions to automate your business processes.

Matrix, our core product which is an operating system designed for the next era of cloud, machine-learning and internet of things. It is designed to orchestrate massive distributed infrastructure so that they become self-healing, self-organising, and self-adaptable.

How we work

Step 1: Analyze your Business Domain

We will first launch a study into your business model and combine it with insights from your database.

Next we’ll design a plan to integrate structured, semi-structured and unstructured data together which will identify workflows that machine-learning can help automate.

Step 2: Plan your Model and Infrastructure

We specialise in using and building deep neural network architectures to address problems in classifying, detecting, segmenting, or understanding: images, videos or language-based data.

Deep neural networks require significant computational infrastructure to serve double-duty: training and inference. Training makes the neural network learn against existing data. Inference evaluates the neural network against new never-before-seen data.

Step 3: Develop the Backend and Frontend

The most useful outcome from any machine-learning application is visual.

We not only build the backend code to run training and inference, but also the frontend visualisations of the machine-learning results catered towards decision makers and operational requirements.

Step 4: Deploy into Production

Machine-learning applications can be complex to deploy. We are experts at deploying machine-learning infrastructure at scale. We will manage deployment onto the cloud, and monitor both the computational and statistical performance of the machine learning application.


Service Abstraction and Communication Flows

Previously we had talked about service-centric networking. Since then we have now implemented a prototype experiment implementing the ideas from Serval that demonstrates ICMP ping migration involving the Linux iptables...

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Session Types

Working with type systems and communications can be very difficult. In Haskell, messages between threads are usually conveyed through channels that only accept a single type, and as a result...

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Docker's V1 and V2 Image Specification

In 2016, Docker has officially updated their image specification from V1 to V2, adopting a more sophisticated scheme that is inline with OCI Container Image Specification. There are only a...

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Polykey - Distributed Secret Sharing

The Matrix AI team has been developing Polykey, a distributed peer-to-peer secret sharing system. It is intended to manage secrets, passwords, API keys for both humans and machines. Many secret...

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Service Centric Networking

TCP/IP networking relies on IP addresses mapped to a machine to facilitate routing through the Border Gateway Protocol (BGP). This mapping is usually done through the DNS, which maps...

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