Understanding Client Needs
We study the case to understand clients needs and how AI can help with their problem.
Data Gathering
Data usually comes from different sources and needs to be standardized and filtered based on quality and other criteria.
Data Tagging
In most cases, ML is not only about data but tagged data. At this stage, we define how to tag and identify the data, choose the best tool to do it, and we tag it.
Model Training
We search for the model that suits best the specific case and its characteristics.
Deployment
Be it on cloud or edge solution, we pack the model and software code around it and leave it production-ready.
Analyze Results
Once the solution is in use, we monitor its performance and impact on the business to see where the focus should be placed on the next iteration.
