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Optimisation of Machine Learning pipelines and hardware is a key component of our work here at Datatonic.  A Machine Learning pipeline is generally de...

Author: Christelle Xu, Business Analyst Leveraging BQML in tandem with Looker’s semantic layer to build, visualise, and deliver powerful predictions A...

Dataset diversity You’ve doubtless come across recommenders before. You’ll certainly have been on the receiving end of a few. The question is, what do...

Discover what a typical Machine Learning pipeline looks like for Propensity Modelling on Google Cloud Platform, using TensorFlow as our framework of c...

This article describes how we built a generic solution to perform joins of CSV data in Apache Beam. Typically in Apache Beam, joins are not straightfo...

The discipline around providing infrastructure and best practices in machine learning, MLOps, is starting to take shape and is reminiscent of the earl...

London, 9 April 2019 — Datatonic today announced that it has received the 2018 Google Cloud EMEA Services Partner of the Year award. This award was pr...

In the digital world, online companies must embrace data and analytics to ensure their media spend is competitive. Whilst Google Analytics and Adwords...

Experts estimate that 80-90% of data in organisations is unstructured, and the amount of unstructured data such as free-form text, images, audio and v...

The world’s data is expected to grow 10-fold over the next decade. Consequently, more and more sophisticated solutions to store and process data are r...

This story about how AutoML can help with wildlife preservation was also featured on this Google Cloud blog. Over the last 40 years, more than half of...

“Recommender systems” are engines that capture relationships between users and items, with the aim of predicting or suggesting new connections. This i...