During the discovery call with a client, we provide a free AI consulting session on a client’s tech challenges. We develop a detailed roadmap which includes not only transparent pricing but also project stages, delivery timeline, and team breakdown. Upon agreeing on the roadmap, we sign a contract and set up a project team.
We start the project with the initial solution architecture design document which consists of the data processing pipeline, monitoring service & hypotheses, and best fitting Machine Learning / Deep Learning architectures.
Our team adjusted Agile methodologies from web development for Machine Learning / Deep Learning R&D cycle including Kanban and Scrum. To set up the R&D process, we configure the tools and team operations to track, control, and iterate on the progress.
Our team assists with anonymizing and depersonalizing any sensitive user data, gathering and labeling custom datasets via data labeling partners or state of the art models, and cleaning the data for further exploratory data analysis. Once the data is ready, we develop the Machine Learning / Deep Learning Pipeline. We launch training and parameters tuning cycle, then test the model in inference mode and optimize graphs to achieve defined metrics in terms of accuracy & speed.
In parallel with Model Development and Training, we launch the service development process where we merge the entire data pipeline with the Machine Learning / Deep Learning Pipeline. After that, we develop the end API, service, or package which will be used in production. We deploy the solution to any required destination such as cloud, dedicated servers, mobile, or embedded devices.
By integrating monitoring, performance testing, and CI / CD systems, we gather insights on new data, data anomalies, and key metrics that serve as a trigger to continue improving model performance. Once the service is up and running, we document the full solution and train the client’s team to use it correctly by conducting knowledge transfer workshops. When necessary, we develop new features, improve the performance, keep the accuracy high and support the project on the ongoing basis.