Our Process For Machine Learning Projects

First Stage – Concept Validation

This stage is critical and sets the whole project up for success by properly validating the quality of the data and what can really be achieved with it.

First we will demystify the machine learning process and understand your business objectives, explaining which pain points you can and cannot address with AI, and identifying the low hanging fruits where it’s possible to quickly bring measurable results. Then we will explore with you the data available in your organisation and to your organisation, along with its potential for use in machine learning, followed by building a proof of concept to show it’s possible to meet your goals.

Second Stage - Refine and Automate
Here we improve on the previously built proof of concept to increase the quality of insights, bringing in more of the available datasets where appropriate.

Next we package the solution into software you can deploy within your organisation and integrate with existing systems if required.

To visualise your insights, we will integrate one of the current leading visualisation tools such as Microsoft PowerBI or Tableau into your solution.

Third Stage - Production and Support
We optimise and scale your ML deployment according to demand as it enters production, from solutions that are run occasionally on a huge set of data to provide insights to your management team, to solutions that run on a single data record every time a customer visits your busy website, scaling is important and needs consideration.

Ongoing support will evolve your machine learning models as you collect new data.