Service maps are a visual and interactive depiction of how the services interact with each other. They also provide critical health information (such as latency, number of errors, etc.) for each service. As more and more applications are getting deployed as microservices, service maps have become increasingly important. In this blog, we will explore the importance of service maps in an observability solution and cover the basics of this feature in Epsagon.

Dogfooding, aka “eating your own dog food”, is a common practice of organizations using their own products for real-world scenarios. Organizations that do it right enjoy the advantages of early-stage use-case validation, bug hunting, and feedback. Developers who actively use their own product gain confidence and a deeper understanding of their clients. Dogfooding is something we at the Epsagon team pride ourselves on.

At Luther.ai, we use AWS Serverless Stack and Kubernetes for all the core real-time pipelines, and it is a data-driven execution across all AWS Services — ECS, Lambda, SQS, Fargate, etc. With hundreds of services and thousands of invocations, each day presents significant complexity to configure, monitor, review logs, measure latency, etc. For configuration and CI/CD, we use the Serverless Framework for packaging and deploying AWS Lambda functions.

In the previous post of our Serverless Machine Learning mini-series, we discussed how to develop and package a predictive model using scikit-learn. This was to prepare a model that could be used in the future to deploy to AWS Lambda, allowing a developer to call your serverless machine learning model like an API end-point without the need to design and implement an entire API.