Machine learning Ops

The MLOps process starts with data. Data scientists spend most of their time exploring, preparing, and ingesting data. The work continues with identifying features for machine learning models, versioning the data, and splitting it into training, validation, and test datasets. To increase the productivity of machine learning engineers, our blueprints focus on high accessibility of quality data for use with our powerful Analytics platform and ML platform.

Most of MLOps capabilities are focused on model lifecycle management. Data scientists are usually familiar with the first stages of the lifecycle, but face challenges during production deployment of models. The final stages of the lifecycle include packaging of the production model, versioning it, and saving it in a repository. From there, the production model is used to generate insights. The MLOps toolbox should support a variety of machine learning algorithms - from advanced analytics to neural networks and deep learning.

The last mile in MLOps involves model serving - a machine learning model is deployed to production as part of an application or microservice. The deployment options can include cloud, datacenter, or edge. The insight delivery can be done either via Model-as-a-Service or by embedding a model into the consumer application. The model lifecycle doesn’t end there though. The model performance is monitored and the model automatically retrained if needed, ultimately achieving autonomous model operations.