Operations | Monitoring | ITSM | DevOps | Cloud

AWS Step Functions is a service that abstracts distributed applications into state machines, with each state representing a component of an application. Not only does this automatically generate an architectural diagram of your application’s workflow, it also makes it straightforward to reorder your states as well as implement parallel execution, retries, and other tasks.

Cloud Logging is a popular tool to help developers, operators, and other users identify and find the root cause of issues in their infrastructure. With features like the Logs Explorer, you can quickly and efficiently retrieve, view, and analyze logs. To help you get the most out of your logs, we’re excited to introduce suggested queries in Cloud Logging to help highlight important logs, so you can start analyzing and troubleshoot issues quickly.

Welcome to the Serverless world. One of the first things you’ll hear about is AWS Lambda - and you’ll continue to keep hearing about it! While architecture can be serverless without Lambdas involved, it’s very often the key component within a serverless application. In the first post of this 3-part AWS Lambda Handbook series, we run through what is AWS Lambda, dialling back to basics with the various terminology, how to create a Lambda function and how to run it.

More and more companies are beginning to turn to DevOps and the cloud as a way to improve their software teams. Whilst it used to be that development and operations were seen as separate, that view has now changed. Linking the two leads to better communication, faster development times, and the ability to stay on top of things.

Cloud adoption has continued to push the momentum on digital transformation. The initial apprehension within enterprises on managing the disruption caused by the ongoing pandemic has slowly waned. Enterprises now have a clearer picture of the situation and are moving forward. The need for online collaboration and online meetings has forced most enterprises to rely more on cloud services for managing their workforce and to provide an environment that allows employees to work remotely.

We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

Working with Serverless computing is like riding an electric bike. You get speed, flexibility, automatic assistance to scale with ease. Development is usually hassle-free because you can focus on code and only pay for usage of the service. Except when your users hit an error. Debugging that issue feels like your bike’s battery just died while climbing a steep hill.