Due to the structure of Machine Learning solutions, performing several explorations and evaluation tasks is inevitable. Due to this, we apply agile principles when developing our solutions. This allows us to:
The steps below are iteratively carried out according to the needs of the business challenge and the solution.
We first clarify the business challenges your organization faces together with experts and involving the end-user. We use the Value Proposition Canvas as our main business strategy tool to first understand what is challenging your companies goals.
After understanding your business challenge, our engineers use the relevant data to build a model that best serves the solution. We extract hidden information from the data and represent it as a computational model.
The model will be integrated into the existing software landscape and processes according to your existing guidelines. The process integration involves training, coaching and working together with teams to ensure a seamless adoption.
Last but not least. When the solution is in use, the gathered feedback serves us in two ways. First, we can use the feedback to optimize the model. Secondly, the feedback collected through traditional KPI dashboards shows the value of the solution and serves as input as to what should be focused on next.
By using Agile Machine Learning we ensure that we rapidly deliver tangible results with a measurable competitive advantage to your business. Iterative development enables your organization to respond and adapt according to business changes as well as new insights. We will be by your side to identify the problem at hand, develop an ML solution and ultimately help you scale this solution to your needs.
