Getting your data ready for AI and Machine Learning.

Due to the perceived technical complexities, AI and Machine Learning remains unreachable for businesses other than the biggest and most technically advanced. This doesn't need to be the case. We can make it useful for a wider range of businesses.

How we do it

AI and Machine Learning works when the underlying data is clearly understood and in a usable and configurable form. Unfortunately, a lot of industrial data is raw, messy and lacking context - leaving a gap between "available data" and "understood data".

We can make it ready so that you can realise the massive potential of AI and Machine Learning. Here are the steps we follow:

Frame

The key to building any effective AI model is to be able to frame the problem clearly -with specifics of "what" and "why". Once this is done, the "how" becomes a lot easier.

Map

Using Thred's unique toolset, we will visually map the required data sources; in a way that shows where they are located, what they represent, and how they relate to one another.

Build‍

Building the model involves selecting the right algorithms for the learning objective outlined in step 1 and the data that will be used. Then we will adjust or "tune" the hyperparameters to go into training the machine learning model.

Test and Implement

Our testing and implementation approach - especially early on - is highly collaborative with the client. Getting ML models to work requires a lot of iteration, feedback and review. That is why our style of engagement is key to our clients building the necessary confidence to trust the models that have been developed.

Review and Improve

All our AI / Machine Learning models go through a rigorous review process that ensures that we are getting answers to the initial problems we framed. This is tested both technically and commercially. And like any other part of our clients' business; it should always follow a process of continuous improvement.