Model Development

Data Science and Machine Learning Project

AI systems are always built on models, which define the same challenge and problem. Different approaches, methods, and processes are required for other use cases. Designing an AI project should always focus on the needs of the business and be related to clear use cases.

Arrow Right Custom Attribution Models 
Arrow Right Customer Lifetime Prediction 
Arrow Right Churn Prediction 

Project Plan

Arrow Right Simple Identification and prioritization of individual issues & use-cases  
Arrow Right Simple Preparation of project plan and ensuring data availability  
Arrow Right Simple Check of data quality and preparation of data  
Arrow Right Simple Exploratory data analysis and execution of advanced analysis 
Arrow Right Simple Presentation of model results based on new test data 
Arrow Right Simple Preparation of machine learning infrastructure & pipelines to use the new trained model for batch or online prediction 
Arrow Right Simple Training of the stakeholders for using the new infrastructure and processes 
Arrow Right Simple Process hand-over. Definition of maintenance and support process and further iteration stages (including new use-cases)