To maximize a project’s chances of success and reduce the risks often associated with ML/IA projects (especially those related to data scarcity), we start by conducting a feasibility study. We take the time and work closely with you to define the requirements and objectives of the project. We analyze the business process(es) involved, assess the availability and quality of data and consider whether additional data sources can be leveraged. At the end of this preparatory phase, we deliver a report and a roadmap detailing the solution’s development scope, the technologies involved and anticipated results.
Based on the available data and the defined objectives, we select and elaborate the most suitable algorithm(s) to develop the Machine Learning model. Data quality is essential for efficient modeling, we therefore perform any necessary data treatment (collection, cleaning, structuring). Finally, we develop, evaluate and refine the ML model until it reaches the performance and reliability objectives.
To transform the validated ML model into an operational AI solution, we design an automated ML pipeline (from production data collection to model monitoring) tailored to the specifics of your facility. In consultation with your teams, we integrate the solution into your systems and ensure that the integration does not impact the performance of the model nor the integrity and operation of your informational ecosystem.