Operations | Monitoring | ITSM | DevOps | Cloud

In 2021 it’s common practice for businesses to use a pay-as-you-go/use pricing model. It’s no different with Amazon. It’s also the primary reason why this article is such an important read for all those looking to reduce their AWS Lambda costs. In this article, we will go over six actionable strategies to optimize the cost relating to our AWS Lambda usage. One of the main reasons for choosing to move into the cloud is the ability to reduce costs.

We’re officially cool! Dashbird is extremely proud to be named as a Cool Vendor by Gartner in Monitoring, Observability, and Cloud Operations in their 28 April 2021 report on “Cool Vendors in Monitoring, Observability and Cloud Operations”. “Dashbird provides a novel approach to observability for serverless applications that run inside an AWS environment.

In this article, we’ll be taking you through the steps and what to bear in mind in each stage of migrating to serverless – from preparation to migration and post-transition.

This is a basic introduction to Lambda triggers that uses DynamoDB as an event source example. We talk a lot about the more advanced level of Lambda triggers in our popular two-part series: Complete Guide to Lambda Triggers. If you want to learn more, read part one and part two. We’re going back to the basics this time because skipping some steps when learning something new might get you confused. It tends to get annoying, or it can even make you frustrated. Why?

For the people using Azure Service Bus namespaces – we often have the need to ensure the Azure Service Bus Performance by testing our system integration by generating some test messages on the Azure Service Bus resources. You might need this for QA/Development for performance testing, load testing etc. This blog will explain how to simulate the test environment using Serverless360 to check Azure Service Bus performance and its throughput.

Useful AWS hacks and tricks that will save you time and money. If you work a lot with AWS, you probably realized that literally, everything on AWS is an API call; hence everything can be automated. This article will discuss several tricks that will save you time when performing everyday tasks in the AWS cloud. Make sure to read till the end. The most interesting one is listed at the very end 😉

TL;DR: KFServing is a novel cloud-native multi-framework model serving tool for serverless inference. KFServing was born as part of the Kubeflow project, a joint effort between AI/ML industry leaders to standardize machine learning operations on top of Kubernetes. It aims at solving the difficulties of model deployment to production through the “model as data” approach, i.e. providing an API for inference requests.

Towards the end of 2016, it all started with developing a simple platform to manage Microsoft Azure Service Bus namespaces. The then classic Azure portal had limited capabilities to manage Azure Messaging resources like Service Bus Queues and Topics. Paolo Salvatori developed and managed a community tool called Service Bus Explorer. We identified that there are some challenges or limitations in managing and monitoring Azure Messaging resources using the above two.