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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.
MLOps practices aim to standardize and streamline the lifecycle of machine learning solutions in production.
We design and build an automated process that takes data and code as input and produces a trained ML model as the output.
Once a suitable model is found, we help decide how it will be served and used in production and implement the chosen deployment pattern.
A deployment pipeline automates the process for getting ML software from version control into production, including all the stages, approvals, testing, and deployment to different environments.
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.