Productionize AI-enabled
applications in the
enterprise-grade manner

Fast, resilient and reproducible
deployments

Deploy AI-driven solutions to production reliably and efficiently with automated pipelines and workflows.

Complete ML model lifecycle
management

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.

ML model orchestration
and governance

Ensure reproducibility with consistent version tracking of input data, machine learning models and model hyperparameters.

Complete ML lifecycle
orchestration

MLOps practices aim to standardize and streamline the lifecycle of machine learning solutions in production.

Automating the end-to-end
lifecycle of ML applications

Machine Learning pipelines
development

We design and build an automated process that takes data and code as input and produces a trained ML model as the output.

Deploying and serving models

Once a suitable model is found, we help decide how it will be served and used in production and implement the chosen deployment pattern.

Continuous Delivery for
Machine Learning

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.

Deploy AI-driven solutions
with cloud native foundations

Cloud-native foundations

Leverage our expertise in domains of cloud platforms, containers and cloud-native automation toolset to develop and deploy scalable, available and secure ML solutions.

From DevOps to MLOps

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.

Observability and monitoring

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.

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