Acquiring the capacity to rapidly extract, consolidate and enrich data sets tailored to the specific needs or AI use cases provides a greater flexibility and significantly reduce the risks associated to machine learning projects.
Machine learning requires to transform raw data into features whose properties are well-adapted to what machine learning algorithms can handle. Features are the input variables that feed learning algorithms.
On large scale projects migrating ml models to production always generates some burden and keeps IT teams under pressure. Well designed scoring pipelines can greatly facilitate the migration to production by keeping all data stakeholders aligned. Documentation and processes play a key role in the capacity of a data science team to successfully deploy machine learning models to production.