Production Data Science

Data science platforms to support the AI and machine learning lifecycle and enable scalable model performance

Your talented data scientists have trained amazing models, most often in Jupyter notebooks. Getting those complex and sophisticated models deployed into secure and scalable cloud architecture is a challenge. Equipping your data science team with platforms to support their workflows around model versioning, model deployment, model retraining, and model monitoring is essential to their productivity.

We design and implement data science platforms and cloud architecture to support the data science lifecycle and enable scalable model performance for production applications.

We have experience with the major cloud vendor tools and with cloud agnostic, open source tools to connect data scientists to scalable compute and storage resources via Kubernetes. 

Our solutions allow easier design, creation, versioning, and deployment of complex data pipelines. To support full MLOps solutions, we incorporate model monitoring and automated retraining approaches. 

If needed, we offer managed services to support our clients that have not yet built their Data Engineering and ML Engineering teams.

Our solutions for Production Data Science

Connect data scientists to scalable compute and storage resources

Implement custom model monitoring

Design and implement data engineering pipelines

Automate model retraining

Design, create, version, and deploy complex models (MLOps)

Examples of our work supporting Production Data Science

Next Steps

From strategy to deployment, we partner with you where you are in the AI/ML lifecycle.