How scaling vertically can make horizontal scaling achievable.
This article is for engineers who use Apache Parquet for data exchange and don’t want nasty surprises.
On the 27th May, Product Specialist Amy Lees and Machine Learning Engineer Christos Hadjinikolis analysed the quality of crude imported into the ARA region, a key refining hub in Northwest Europe, using Vortexa’s Software Development Kit (SDK). This blog investigates further into their methodology.
In our last tech blog post, we detailed how making a very small change to some complex production Python code, introducing Rust to handle a code hot-spot, led to dramatically better performance. Now is the time for more details.
There are times when adopting a standard approach just isn’t good enough. This post is about making minimal changes for maximum effect where it matters.
With the launch of Vortexa Academy, we demonstrate one of the Python for the Energy Industry course notebooks to show the powerful ability of the Vortexa SDK and how it can assist in daily analytics and trading decisions.
We launch a new functionality for the PythonSDK of embedding our notebooks in your browser, making it easier for users to run notebooks faster and seamlessly.
Provocative title, but what does it mean? Let’s take a journey to get to the meaning, and how this impacts not only technology, but human behaviour.
A curious journey into the world of geohashes and microprocessor caches, where code can be made to run faster without changing it.
Sometimes writing your own algorithm out-performs a library by 3 orders of magnitude
Vortexa’s CEO Fabio Kuhn delivered a presentation on Scaling Human Energy Market Expertise with AI, as part of ZE Powergroup’s Global Tech Summit. In the presentation, he discussed the digitisation of the industry and how this affects making faster and smarter trading decisions.
Using Data Science to create unprecedented visibility into energy markets and make the world slightly better.
This is the second article of this series, outlining how to think about data processing code in the Python world, and more specifically, how we can improve our code performance while writing legible, maintainable, production-quality code.
In this article, our Head of Data Services focuses on different practices to make sure that our data can be trusted, how we keep it recent and always accessible by adopting the mindset of site reliability engineering.
A look into the journey of one of our analysts learning Python and implementing his skills with the Vortexa SDK.
The first in a two-part series of articles outlining how to think about data processing code in the Python world, and how we can improve our code performance while writing legible, maintainable, production-quality code.
The final chapter of our Satellite Image Segmentation. This piece is an analysis on how to identify where ships are within a satellite image, building a U-Net from the encoder developed in Part 2.
Towards a fully convolutional network, for more interpretability (example of CAM attention), early localisation properties, and capability to evolve into an image segmentation model. In the process, we’ll delve into the technique of transfer learning.