MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning. It is the creation of an automated environment for model development, model retraining, drift monitoring, automation of pipeline, quality control, and governance of a model into a single platform. By adopting MLOps, any organization working with AI can automate data pipelining, model development, packaging, versioning, and monitor model accuracy. It can ease the work of data scientists and ML operations teams, enabling them to focus on higher-value creation.
Synthesize features in every way you can imagine to bring out hidden patterns in data.
Record Model training history, training data and structure for governance and future design ideas.
Simplify the process of creating and pushing the model into production with a simple, efficient versioning and registration system.
Enable fast retraining with either the same model design or create a completely new structure from previous architecture.
Store all the data that deviates from standard patterns to course correct your model.
Send data into the production pipeline with a simple interface.