75% reduction in downtimes for AI food waste solution

Heavily relying on real-time tracking and analysis, Winnow is committed to providing crucial day-to-day insights to its users. Any interruptions or downtimes can impact the data's quality and overall tool's efficiency.

Tight interdependencies between Docker containers caused bottlenecks during product updates and deployments. It also restricted system flexibility and agility, creating additional challenges in adapting to swift operational changes.

As the AI models evolve, incorporating new versions into the existing working product without disrupting its operations becomes challenging. Inconsistencies between the deployed model and the latest version could rob users of many benefits.

Initially, the client hosted their 10-component application on the AWS server. With each container relying on the others, a shortfall in any component risked system failure. To address this, our team transitioned from AWS server to AWS ECS, enabling automated version deployment with a single click on the Jenkins dashboard.

Allowing the system to scale up and down depending on its needs, our engineers developed a set of specific scripts. IaC approach to automation helped to make the Winnow infrastructure more flexible, making it easier to validate changes, create versioned releases, and apply a variety of coding best practices.

To automate routine software development work, our team set up Jenkins and a CI/CD Pipeline. Now, there is no need to stop an old docker image and start a new one manually since this can be done with one button in the Jenkins dashboard.