Machine Learning Operations (MLOps) is based on DevOps principles and practices—including continuous integration, delivery and deployment—that increase the efficiency of workflows. MLOps applies these principles to the Machine Learning process, with the goal of:

● Faster experimentation and development of models

● Faster deployment of models into production

● Quality assurance and end-to-end lineage tracking

In this White Paper, you will be introduced to the different everyday life challenges of a Data scientist during the lifecycle of a model and their real world solution and area of improvment!

Florian Laroumagne

About the author

Florian Laroumagne

Senior Data Scientist & Co-founder

Engineer in computer science and applied mathematics, Florian specialized in the field of Business Intelligence then Data Science. He is certified by ENSAE and obtained 2nd place in the Best Data Scientist of France competition in 2018. Florian is the co-founder of, a startup specializing in the automation of Machine Learning. He is now leading several applied predictive modeling projects for various clients.