Spot by NetApp: Cloud automation and optimization enhanced to deliver cost fluctuation predictions

Qwak was engaged to streamline the transition of the team's ML models to production and bridge the gap between data science and machine learning engineering teams.

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.

About Spot by NetApp

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

XGBoost based on Elasticsearch
Linear regression based on S3 data with daily retrain

Model Frameworks

No items found.

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

Amiram Shachar
VP
5X

Faster time to ML model deployment

85%

Less issues in production

5

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