A great AI or ML model is worthless if it can't be delivered in a scalable, cost effective and technically sustainable way. Our data science consulting practise excels in the design and implementation of machine and deep learning models for the analysis of data and media at scale.
Data Science seems to have something mystical about it, to the extent that enterprises have almost come to consider it as a magical solution to impossible problems. The reality is that behind DS there are tons of unfashionable data engineering and analysis tasks, before statistical models can be applied to the problem.
Our team of talented experts here at Data Language can leverage years' worth of technical excellence to ensure your AI solution can actually be scaled and delivered at the highest standards of software engineering.
Unlock the full potential of your data through early discovery, analysis, and visualisation.
With so many variables to tweak, rapid feedback and short iteration cycles are imperative to an effective delivery.
As with any piece of code, statistical models also benefit from engineering best practices for delivering at scale, with controlled total cost of ownership.
Read our white paper on rethinking how you do data science in your organisation:
Data Language helped Euromoney distill a broad and complex enterprise engineering problem into a deliverable technical and data strategy. I would be happy to recommend them to anyone.
Scalable enterprise data science is 10% AI and 90% engineering.
Our Tagmatic text classification AI platform scales to >100k parallel predictive micro-models, and returns an ensemble prediction result in under 1 second.
We benchmarked our Tagmatic service against Hansard data: a corpus of ~200,000 parliamentary interventions for the 2005/6 period , classified with ~7200 categories, F1 scoring a whopping 0.83