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.

In this article I’ll be sharing my own lessons learned with Dashbird in building reliable Well-Architected serverless applications and the highest impact areas to focus on no matter where you are in the Serverless journey.

When you build your application on top of Lambda, AWS automatically scales the number of “workers” (think containers) running your code based on traffic. And by default, your functions are deployed to three Availability Zones (AZs). This gives you a lot of scalability and redundancy out of the box. When it comes to API functions, every user request is processed by a separate worker. So the API-level concurrency is now handled by the platform.

The “as a service” business model continues to grow rapidly, largely thanks to the rise of cloud computing. “As a service” offerings deliver IT products and technologies such as software, hardware, and data storage to consumers via the Internet, rather than having to install or manage them themselves. Serverless and containers are two such “as a service” technologies that have seen increasing adoption in recent years.

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring? Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible and measuring resource consumption for each invocation is possible.

If you’ve heard of the term “serverless architecture” but you have no idea what it means, don’t feel bad. This buzzword (buzz-phrase?) has been on the rise for the past few years, and it doesn’t look like it’s going to stop. More and more cloud companies are trying to promote serverless architectures for businesses. There are plenty of benefits to using a serverless architecture for your business.

Want to know what actually goes on under the platform and behind the screens at Dashbird? We recently sat down for a Q&A with our CTO, Marek Tihkan, on leading and managing an engineering team. Today, we speak to Alex, one of the engineers on the Development team and the brain and elbow grease behind our newly launched Dashbird Atlas real-time 3D map of your entire serverless environment.