Deploy AI-driven solutions to production reliably and efficiently with automated pipelines and workflows.
Control the full lifecycle of the machine learning models including development phase with training, packaging, and validation, and production phase with deployment, monitoring and retraining.
Ensure reproducibility with consistent version tracking of input data, machine learning models and model hyperparameters.
Leverage our expertise in domains of cloud platforms, containers and cloud-native automation toolset to develop and deploy scalable, available and secure ML solutions.
MLOps concepts share a lot with DevOps practices both conceptually and in the domain of tools and technologies. Apply DevOps approach and toolkit to successfully deploy AI-driven applications to production.
Monitoring production AI systems is essential to ensure they are healthy, performant and can be operated in an uninterrupted way. Leverage modern observability tools to track and visualize standard metrics like latency, traffic and errors, as well as model prediction performance.