At the outset of the project, we create a solution architecture design document that includes the data processing pipeline, monitoring services, hypotheses, and the most suitable Machine Learning or Deep Learning architectures.​​

Our data science and AI development team adapts Agile methodologies, originally designed for web development, for the Machine Learning and Deep Learning R&D cycle. This includes incorporating Kanban and Scrum techniques.​

To establish the R&D process, we configure the necessary tools and streamline team operations to effectively track, manage, and iterate on progress.​

Our ML development team starts by anonymizing and depersonalizing sensitive user data. ​

We then gather and label custom datasets using data labeling partners or advanced models and clean the data for exploratory analysis. ​

Once the data is prepared, we build the Machine Learning or Deep Learning pipeline. ​

We proceed with the training and parameter tuning cycle, followed by testing the model in inference mode and optimizing it to meet accuracy and speed metrics.​

Concurrently with Model Development and Training, we initiate the service development process. ​

This involves integrating the complete data pipeline with the Machine Learning or Deep Learning pipeline. ​

We then create the final API, service, or package for production use. ​

The solution is deployed to the necessary environment, whether it be cloud, dedicated servers, mobile devices, or embedded systems.​

We integrate monitoring, performance testing, and CI/CD systems to collect insights on new data, data anomalies, and key metrics.​

These insights drive continuous improvement of model performance. ​

Once the service is operational, we document the entire solution and conduct knowledge transfer workshops to train the client’s team on its use. ​ ​

We also provide ongoing support by developing new features, enhancing performance, maintaining high accuracy, and ensuring continued project success.​