We are seeing life sciences organizations struggle to operationalize data science. These organizations are hiring data scientists with incredible skills and domain knowledge. They are implementing amazing models in Jupyter notebooks but need help to deploy these models into scalable production environments. Further, we needed a cloud agnostic approach that was cost effective.
We created a cloud agnostic data science platform to reduce barriers that prevent companies from getting their ML code into production. Our platform is based on open source tools, like Kubeflow, and connects data scientists to scalable compute and storage resources via Kubernetes. This allows easier design, creation, versioning, and deployment of complex data pipelines. Additional differentiators for this platform include lower cost versus other MLOps solutions and the ability to be tailored to specific use cases. For example, we can incorporate custom model monitoring and automated retraining approaches with custom dashboards that align with specific needs/objectives.