Production Data Science

Deploying complex data models into a secure and scalable cloud environment can be a challenge, even for the most experienced data scientists. That's why we specialize in designing and implementing customized data science platforms and cloud architecture to ensure the productivity of your data science team.

Our expertise encompasses major cloud vendor tools and cloud-agnostic open-source solutions, enabling data scientists to access scalable compute and storage resources via Kubernetes. We simplify the process of designing, creating, versioning, and deploying complex data pipelines with user-friendly solutions that support your team's workflows. We incorporate model monitoring and automated retraining approaches to support full MLOps.

For knowledge intensive applications, we design and implement graph databases and document stores to support large scale biomedical knowledge networks. We develop custom graph analytics and user experiences to support biologists with actionable intelligence.

Our goal is to help you achieve a smooth data science lifecycle by providing customized data science platforms and cloud architecture that support your team's workflows, including model versioning, deployment, retraining, and monitoring. For organizations that haven't yet established their Data Engineering and ML Engineering teams, we offer managed services.

With our support, your data science team can focus on what they do best – training impressive models – while we handle the complexities of deploying and scaling those models in a secure cloud environment.