Deployment and Operations

Deploying machine-learning models takes about 25% of data scientists’ time, while 75% of models never go beyond the experimental phase, resulting in a significant productivity bottleneck. Automated deployment and retraining processes decrease the time to market and reduce operations cost. This is the subject of machine-learning operations - or in short MLOps. MLOps is an extrapolation of the DevOps approach to include the machine-learning modelling life cycle. Models degrade over time because the input changes. MLOps introduces continuous retraining, model monitoring and evaluation of performance. It saves a version history not only of code, but also of data and models. Versioning of data allows data scientists to keep track of where their data came from, and versioning of models allows to efficiently keep track of the model quality in the development process. We help you to streamline your deployment and operations process with model-training pipelines (TFX, MLflow, Pachyderm, Kubeflow), registry, serving, monitoring and CI/CD orchestration. Send us a quick note via email or give us a call to discuss your deployment and operations setup.