Succeeding in engeneering Data Science models with a smart approach
Our client, an industry leader in the automotive sector, has always been at the forefront of offering solutions with high technological value. Thanks to a data-driven business and the importance attached to Data Science, it represents an example of world excellence in its field.
We assisted the Data Scientist team in defining a solid architectural and methodological framework which would allow them to focus their efforts exclusively on functional issues, and to minimize them on the more technical issues. To do so, we defined a methodology which brings the Data Scientists’ more typically business realm closer to the traditionally IT realm of the Data Engineers: this “smart” approach, supported by the use of automated pipelines for the Continuous Integration / Continuous Delivery and container processes which guarantee segregation of the executed code, allows the Data Scientists to maintain full ownership of the entire process, and eliminates the rigidity typical of scenarios in which the models are conceived, re-engineered, and then put into production by different entities.
The constant support of methodology, organization, and tool guidelines in fact makes it possible to rely on Data Science models with extremely solid foundations, mitigating the consequent risks of poor performance.
The advantages of a “smart” approach are significant and numerous. For our client, in particular, it has enabled:
- Better use of resources thanks to a more structured work methodology;
- Skillful use of hyped technologies;
- Replicability of development environments, of manual or automatic tests, and of production;
- Full ownership of the entire process by Data Scientists;
- Increased traceability of implemented actions;
- The ability to test a preview release of new versions of the models in production.