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Jess and Carmel recently gave a talk at Azure Oxford on “Combatting illegal fishing with Machine Learning and Azure – for less than £10 / month). The recording of that talk is now available for viewing!

The talk focuses on the recent work we completed with OceanMind. They run through how to construct a cloud-first architecture based on serverless and data analytics technologies and explore the important principles and challenges in designing this kind of solution. Finally, we see how the architecture we designed through this process not only provides all the benefits of the cloud (reliability, scalability, security), but because of the pay-as-you-go compute model, has a compute cost that we could barely believe!


Building a proximity detection pipeline

by Carmel Eve

At endjin, our approach focuses on using scientific experimental method to support the creation of fully proved and tested decision making, and the use of scientific research to support our work. This post runs through how we applied that process to creation a pipeline to detect vessel proximity.
This is an example which is based around the project we recently worked on with OceanMind. In this project we helped them to build a #serverless architecture which could detect vessel proximity in close to real time. The vessel proximity events we detected were then fed into machine learning algorithms in order to detect illegal fishing!
Carmel also runs through some of the actual calculations we used to detect proximity, how we used #data projections to efficiently process large quantitities of incoming data, and the use of #durablefunctions to orchestrate the processing.


Overflowing with dataflow part 2: TPL Dataflow

by Carmel Eve

This is the second blog in a series about data flow. This post delves into TPL dataflow.

The task parallel library is a .NET library which aims to make parallel processing and concurrency simpler to work with. The TPL dataflow library is specifically aimed at making parallel data processing more understandable via a pipeline-based model.


Overflowing with dataflow part 1: An overview

by Carmel Eve

This is the first blog in a series about dataflow. The series focuses on TPL dataflow, but this post gives an overview of dataflow as a whole.

The crucial thing to understand when using dataflow is that the data is in control. In most conventional programming languages, the programmer determines how and when the code will run. In dataflow, it is the data that drives how the program executes. The movement of data controls the flow of the program.