Spot by NetApp: Cloud automation and optimization enhanced to deliver cost fluctuation predictions
Qwak played an instrumental role in refining the team's ML model deployment processes, fostering seamless collaboration between data science and machine learning engineering factions. NetApp's Spot offers an avant-garde solution in cloud automation and optimization, enabling enterprises to optimize their cloud investments via superior CloudOps expertise. Through strategic investments in Data Science and Machine Learning, Spot not only amplifies the value for its users but also provides advanced predictive analytics for infrastructure shifts and forthcoming cloud cost variations.
Spot by NetApp transforms cloud operations through innovative machine learning and analytics, automating and optimizing infrastructure for optimal, scalable, and cost-effective performance. The platform maximizes cloud investments with cutting-edge technology and predictive insights into infrastructure and cost fluctuations.
Industry
SaaS, Cloud Operations
Use Case
Model Frameworks
Qwak helped us to make a paradigm shift to our data science operations and put us in a place we were trying to get to for a while now. I feel that the real value in Qwak is the fact that it actually ensures that your models make it into production really fast. We now deliver new models quickly and efficiently and with much less friction along the process
Faster time to ML model deployment
Less issues in production
New models running in production within 4 weeks
Challenges
- Deliver models to production efficiently and in a timely manner
- Enhance Data Science version deployment with less engineering and/or DevOps dependencies
- Test model versions and feature creation without impacting production environments
- Centrally monitor model performance metrics and control changes. Align feature data pipeline
Solutions
- Deliver models to production efficiently and in a timely manner
- Enhance Data Science version deployment with less engineering and/or DevOps dependencies
- Test model versions and feature creation without impacting production environments
- Centrally monitor model performance metrics and control changes. Align feature data pipeline
