Systems | Development | Analytics | API | Testing

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

If anyone's ever been to AWS ReInvent in Vegas before, you'll know it's a crazy ride. This year we missed out (at least we have cleaner consciences and healthier wallets). But the high quality of content hadn't changed. We've been binging on sessions ‘til the bitter end (it officially ended Friday). So for our community, here is a summary of a few talks related to Apache Kafka.

“We’ve seen two years’ worth of digital transformation in two months” said Microsoft’s Satya Nadella. Due to COVID-19, digital transformation roadmaps have been deleted, redrafted, doubled down and accelerated by up to a decade. Traditional companies are moving by osmosis towards streaming technologies such as Apache Kafka to kick off new digital services. But how much should it cost to experience 2030 in 2021?

We created the Terraform API gateway module to help you follow DevOps best practices while implementing Kong using infrastructure as code (IaC). Terraform is an open source tool that allows you to implement IaC using a declarative declaration definition. This Terraform module is the reference platform maintained by Kong for potential and existing customers to quickly set up both Kong Gateway and Kong Enterprise for demo and PoC environments.

At the heart of Kafka is real-time data. With data at the center of any Kafka environment, it should be the area that gets the most attention, but typically it gets the least. This happens because we see most organizations split their Kafka efforts into three areas: infrastructure, monitoring, and data operations.

Welcome to the Bugfender winter newsletter! We hope you had a great Christmas and new year. This quarter we’re bringing you a lot of insightful information on our blog. Take a good cup of coffee and find our latest recommended articles below. But first, let’s kick-off with the latest updates as we do on every quarterly newsletter: We hope you find all these updates useful!

For quick, scalable, highly-available web services, few options compare to AWS Lambda. Just provide your code, add a little configuration, and you're done! In this article, Milap Neupane will introduce us to Lambda, show us how to get it working with Ruby and the Serverless Framework, and discuss reasons to use — or to not use! — Lambda in production.

Are you running your organization's Apache Kafka on-premise? If you are and you’re still reading this article, it’s more than likely that Kafka is or will be a keystone of your data infrastructure. But it’s also likely your teams are tired of the cost and complexity required to scale it, meaning your honeymoon with Kafka is coming to an end. So what does the imminent migration mean?