Close up of a person's eye

Using gliff.ai to Realise the Potential of OCT Data

The aim of the OCT data project is to create a ground-truth annotated dataset that would be utilised for the future development of fully automated AI tool capable of segmenting and quantifying different features in retina images and scans…

The missing people in MLOps

Artificial Intelligence (AI) and Machine Learning (ML) are changing the world around us and usually for the better. AI aims to train machines to mimic the actions of human experts and to learn from new experiences and new data. In recent years, AI experts have been able to create smart home assistants, clever homes that automatically adjust their environmental settings, and improved movie and product recommendations.

Laptop, book and smartphone chained together

But AI does more than just make our lives more comfortable. AI systems now also sit behind the wheel of a car. AI systems now help doctors make diagnoses. AI systems help scientists design new vaccines and pharmaceuticals. AI is a powerful force for real change in the world.

MLOps — from the lab to the real world

Every month, hundreds of new articles are published in scientific journals that show proof-of-concept AI systems for a range of applications including: automated cancer detection, including at-home tests; automated fall detection systems that allow the elderly and vulnerable to maintain their independence but still be safe; and identifying early signs of diabetes from images that can be taken as part of your regular eye test. But how many of these are actually being used to help improve peoples’ lives? Very few in fact. (See an article by my colleague Bill Shepherd for a discussion sparked by a recent Nature Machine Learning paper that discussed just this.)

From: https://www.tecton.ai/blog/devops-ml-data/

What’s missing in MLOps? The experts!

Excellent! We’ve brought together data scientists and engineers so we can now productionalise our AI — right? Well, this is where things get complicated. Current MLOps pipelines largely come from domains where the people described as experts that the AI is trained to mimic are largely everyday people — shoppers or car drivers, for example. In these cases, the data scientists, AI scientists and even citizen scientists are able to ‘annotate’ large data sets with the features of interest.

Enter the experts — collaborative MLOps

First, let’s discuss who these experts are and why we need to include them when developing AI products.

The missing people in MLOps — let’s get them involved now

So, we’ve identified that there are missing people in MLOps, what can we do? Well, let’s get them involved. And that’s what gliff.ai is all about — completing the MLOps pipeline by bringing domain experts, data experts, AI experts, production experts and regulatory experts together in one environment. Only by doing this can we create amazing AI that really changes the world. If you’re interested in finding out more then drop us a line.