AI-Powered ML Innovation for Cloud Platform

A cloud platform struggled to deploy new AI models quickly. They adopted an AI solution for machine learning workflows, slashing deployment time by 5x, reducing production issues by 85%, and enabling the deployment of 5 new models within 4 weeks. This accelerated innovation and gave them a competitive edge.

Client:

Spot by NetApp is a cloud automation and optimization platform that leverages machine learning and analytics to continuously optimize cloud computing resources’ cost, utilization, and availability. It helps businesses maximize their cloud investments by automating the provisioning, scaling, and management of cloud infrastructure.

Problem Statement:

Manual processes for delivering AI models to production were inefficient and time-consuming. It took weeks to deliver a new model, which slowed down innovation and made it difficult to keep up with changing business needs.

There was a lack of visibility into model performance and data lineage. This made it difficult to identify and resolve issues, and to ensure that models were meeting business objectives.

There was a high degree of dependency on engineering teams for model deployment and maintenance. This made it difficult for data scientists to work independently and iterate quickly on their models.

Results:

● 5x faster ML model deployment
● 85% fewer production issues
● 5 new models deployed in production within 4 weeks
● Reduced reliance on engineering teams
● Centralized monitoring and management
● Improved data quality

AI Solution:

To address these challenges, Spot by NetApp implemented Qwak, an AI-powered platform for managing machine learning workflows. Qwak automates various tasks involved in model deployment, including version control, testing, monitoring, and feature management.

Implementation:

● Model Deployment: Qwak’s Model Serving feature streamlined the deployment process, enabling Spot by NetApp to deploy new models with a click of a button. This automated canary deployments, ensuring minimal disruption to production environments.

● Model Monitoring: Qwak’s Model Monitoring feature automatically stores inference data and provides real-time insights into model performance. This allowed Spot by NetApp to proactively identify and resolve issues, ensuring model reliability and effectiveness.

● Feature Management: Qwak’s Feature Store provided a centralized repository for managing features used in ML models. This improved data quality, simplified feature access, and enhanced model reproducibility.

● Team Collaboration: Qwak reduced the dependency on engineering teams, empowering data scientists to independently deploy and manage models. This fostered better collaboration and accelerated the development cycle.

References:

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

Industry: SaaS, Cloud Operations
Vendor: Qwak
Client: Spot by NetApp