End-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML Applications
We follow MLOps practices in model development increasing the quality, simplifying the management process, and automating the deployment of Machine Learning and Deep Learning models in large-scale production environments. It's easier to align models with business needs, as well as regulatory requirements. It applies to the entire lifecycle of ML model development from data gathering, model creation, orchestration and deployment.
