ML Ops

Data Science and Machine Learning Project

Machine Learning Operations aim to streamline the release process for machine learning and software applications. This allows automated testing of machine learning artifacts (e.g. data validation, ML model testing, ML model integration testing) as well as first-class support of machine learning models and datasets as part of CI/CD systems.

Arrow Right Completely automated machine learning infrastructure 
Arrow Right Reduce time for model deployment  
Arrow Right Connect all relevant sources & destinations efficiently  

Project Plan

Arrow Right Simple Preparation of the workshop that includes an analysis of the status quo and current processes and stakeholders. Analysis of the requirement and definition of acceptance criteria.  
Arrow Right Simple Definition of the new infrastructure & machine learning pipelines including deliverables. Detailed and refined Statement of Work (SoW).  
Arrow Right Simple Engineering stage, Moving the new infrastructure & machine learning pipelines from concept to reality.  
Arrow Right Simple Implementation of the aligned use-cases.  
Arrow Right Simple Control of the use-cases and if applicable adjustments to the infrastructure and the automation process.  
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)