Being a powerful player in the wellness & fitness niche, Naring Health handles multiple diet-related services under one roof. On the one hand, they evaluate DNA, microbiome, and metabolic reaction to particular meals or dietary practices to define a sufficient eating program to prevent or treat disease.

As one of the most influential humanitarian organizations, Adra specializes in managing diversified services, each of which is intended to help individuals, communities, and society as a whole. Such a charity-oriented mission undeniably requires a robust technical foundation.

In the modern business ecosystem, it is essential to constantly improve your services in order to engage potential clients and keep pace with the competitors. Growing means consistently examining your current infrastructure for weak areas and considering whether what you have in place is not only meeting the basic needs of your company today but also setting you up for triumph in the increasingly cloud-focused future.

The client experienced the need to automate the deployment process of microservices and simplify the release process. One of the requirements was to migrate Docker containers to AWS ECS and move existing infrastructure to Terraform.

The client had a manual multi-environment infrastructure deployment аnd no continuous integration & continuous deployment automation. That significantly impacted the speed of product development, release, and delivery cadence of new application versions in general. Besides, it required a considerable part of the development team capacity to process it manually.

The client faced the need to automate multi-environment continuous integration and deployment process of microservices, move the front-end part to CDN as well as deploy multi-environment infrastructure as a code.

The client had high costs for development, deployment, and, most importantly, operation of the data platform including Data Lake, Ingestion, and ML Pipelines. The pipelines were mostly running in EC2 instances, which led to the increased cost of operations and required a significant amount of time to deploy and test the pipelines in lower environments.

The client came with a request to automate and unify the deployment process of serverless applications on AWS Lambda. Having manual deployments before, the customer was facing inconveniences and difficulties, including non-uniform environment setup (versions of Serverless, Python, Node, and so on were inconsistent) and inability to control the environment changes in one place.

The client needed to strengthen collaboration within the data scientists team by moving research capabilities into cloud workloads. Besides, they wanted to automate and unify the deployment process of AWS resources. That, in turn, would decrease the time and effort required for a team of data scientists to build and test their models.

Initially, the client’s development team performed continuous integration and continuous deployment processes manually. Consequently, it was time- and effort-consuming to build cross-platform mobile applications. Besides, they used the legacy tools stack – that doesn’t have built-in mobile-specific continuous integration and deployment capabilities.