The Lumiq difference arises because of a well-articulated methodology. We’re aware of the transition that comes with choosing a data reinvention journey. It is as intense as it is transformational. With our clearly defined framework and practices, though, you will feel empowered and in control every step of the way.
Here’s how it works:
Our first meeting point is as freestyle as it sounds. We identify and clearly state the problem at hand, look at the processes involved, and how they might be contributing to the problem. We also nail down the KPIs and carry out a value and opportunity assessment.
Data is in the spotlight from here on. Based on what we learn about the ecology of your business in the previous step, we define the landscape of how data flows in your enterprise. We also outline a strategy for data discovery and implementation of a robust data model.
With our strategy and implementation framework in hand, we’re ready to act on the data. The quality and usability assessment of data is carried out here. Along with this, data wrangling, data transformation, set generation, testing and training of data is also done.
Here, we put the data under a microscope to understand it better. We generate a hypothesis about the data and then test it. Key trends and observations are duly noted while testing in addition to any important and interesting features exhibited by the data.
Now that the true nature of data has been unveiled, it’s time to put the data to practical uses. To begin with, we select algorithms and come up with a variety of modeling options. Then, we validate these models against real-world results to determine the most accurate ones.
The feature mart enables apple to apple comparison i.e. stamping of data as well as models is done to facilitate model to model comparison.
This is when we step into the world with a finished data model. Data pipelines are set up along with structures for infrastructure management. Model hosting and serving, and machine learning operations are also a part of this step.
Not that the data model is out in the field, it’s time to govern performance. We do this by monitoring, audit and curation, A/B testing, exception scenario, reverse EDA, explainability reviews, and dashboards and reports.
As the final step, we take AI and data to your enterprise at large. This is done through model integration with business processes by leveraging REST APIs or building dedicated ML-driven apps.