Jan 8, 2019 | By Taavi Rehemägi
What a year! It started with just me and Mikk, hacking away at our homes with an MVP and a handful of customers. Now, Dashbird is a team of 6 people with a lot of customers, global investors and a vision for the future. Looking back at the year, we’ve had a lot of wins and also our fair share of failures/learning experiences. I’m very excited for the upcoming year, but before we get into that, let’s look back at the year that just ended.
Dec 2, 2018 | By Taavi Rehemägi
AWS re:Invent, the biggest cloud-computing event of the year, ended on Friday and left behind a slew of exciting new features and products for building serverless applications. Let’s summarize what was announced and how those updates can be significant for you.
Nov 4, 2018 | By John Demian
In one of our last articles, we 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.
Oct 19, 2018 | By Taavi Rehemägi
Traditionally in white-box monitoring, error reporting has been achieved with third party libraries, that catch and communicate failures to external services and notify developers whenever a problem occurrs. I’m here to argue that for managed services this can be achieved with less effort, no agents and without performance overhead.
Dec 27, 2018 | By Dashbird
Dashbird integrates closely with AWS to bring visibility into serverless applications. By mapping applications resources, importing CloudWatch logs and integrating with AWS X-ray it's able to provide all pillars of observability with no performance overhead or code changes.
Sep 28, 2018 | By Dashbird