Machine Learning is an emerging discipline. Without the right organisation teams fall into ways of working which hinders their progress. We can help you to stay on track.
We partner with domain, product and data science teams to help to frame business problems for machine learning. Asking the right questions is key to success.
Machine Learning products have different design needs. Poor product design can have an amplified impact on Machine Learning products. We help you to specify a breakdown of tasks so everyone has a clear set of actions.
Problem specification and design are just the start, to achieve maximum success the right person should be in charge of the right task. With our experience you can create an operational setup with low friction and maximum transparency.
With our support you will have a transparent visibility on the state of your product at all times and stakeholders have clear actions and responsibilities.
Insights from real customers
Specifications regularly change on eventual client feedback. In traditional machine learning projects this cannot happen until the model is created and deployed. We will help you to create MVPs and get them in front of customers.
We provide a framework through which closely aligned domain experts and data scientists can scope a meaningful MVP. This framework provides an efficient data acquisition and augmentation pipeline as well.
We show you how to use a simple, standardized and well structured project format to enable high performance data processing and model generation.
The same structure enables you with your devops teams to create a straightforward route for model deployment. You will use your existing business intelligence frameworks for monitoring and reporting according to your predefined product needs.
This enables an MVP solution to quickly test assumptions and clarify product direction and set the stage for good operational practices.
Machine Learning models are live products that need continuous attention to avoid performance deterioration and to allow you to extend the MVP to be fully featured.
Through the established monitoring processes, your team will gain actionable insights into both performance and new opportunities. Continuous reporting enables your business leadership to make better informed resource allocation decisions.
The closed loop alignment between your domain experts and your data scientists supports efficient data acquisition both for maintaining peak performance and establishing new features.
Armed with the newly acquired data, Data Scientists can quickly create and deploy new ensemble models through the established channels
We provide light touch support to ensure your Machine Learning product remains on track and your team have the support they need to deliver. Our process enables you to make more granular product lifecycle decisions to maximise the impact of your Machine Learning activities.