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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.
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)
Data science platforms and cloud architecture to support the datascience lifecycle and enable scalable model performance
Cloud-scale bioinformatic and data science solutions for faster and more successful drug development
Full stack AI applications for connected medical devices and Software as a Medical Device (SaMD)
Cloud-scale bioinformatic and NLP solutions for faster drug development
