A Fortune 100 Pharma and Lifesciences firm engaged in the research and development, manufacture, and sale of various products in the healthcare field. Their products include pharmaceuticals, baby care products, medical devices, and beauty and women’s health products.
Research:
Have challenges in
- Pod level cost chargeback in Kubernetes clusters
- Service level expenditure analysis
- To analyze their overall project & Model expenditure
- Implement Cost aware model runs, and optimize cluster configurations.
Envision:
- Analyzing the existing Kubernetes cluster arrangement.
- Visualizing the pod level and project level cost spread out with breakdown charts.
Actualize:
- Can optimize their cloud spending with intelligent recommendations.
- Can predict their future projects’ cloud infrastructure requirements and improve efficiency.
- Implement cluster-level config changes
Launch:
- Visibility to pod-level spending.
- Transparency in the project total expenditure(which is not available with the native CSP).
- 85 % Utilization of cloud assets and intelligent scheduling of model runs, results leading to AI-driven model run times for ML Ops platforms.
- Savings in excess of >18 % Costs at Project Level.
