It’s easy to take continuous integration (CI) and continuous delivery/deployment (CD) for granted these days, but these have been transformational concepts that have drastically changed the face of software development over the past thirty years.
While CI/CD is synonymous with modern software development best practices, today’s machine learning (ML) practitioners still lack similar tools and workflows for operating the ML development lifecycle on a level on par with software engineers. For background, follow a brief history of transformational CI/CD concepts and how they’re missing from today’s ML development lifecycle.
Building, running and scaling SaaS demo systems that run around the clock is a big engineering challenge. Through the power of traffic replay, we scaled our demos in a huge way. A few weeks ago we launched a new demo sandbox. This is actually a second generation version of our existing demo system that I built a few months ago (codename: decoy). Because the traffic viewer page shows the most recent data by default, you need to constantly be pumping new data in there. Any type of real-time SaaS system is going to have a similar requirement. So this needs to be planned.
Anyone who's worked in technology has likely hit on the "build vs. buy" question at some point. Should you build your own custom solution to meet the exact requirements of your business? Or does it make more financial sense and save time if you use a third-party vendor? Let's use an example. You are a tech lead at a company that delivers a platform for online learning.
This blog is an attempt to help you understand DevOps and MLOps and their differences and similarities.
In this blog post, we’re answering audience questions related to DevOps, team, and culture from our ‘Building Mobile Apps At Scale’ webinar with Gergely Orosz, Pooja Bhaumik, and Franz Busch.
We love abstractions. We want to make things easier for developers, teams and end users. In doing that, sometimes we build things a little bit too complex for those who don’t already understand the pain points for which the abstraction layers were built. Kubernetes is an example of this; it solves a very real, very painful problem, but it is notoriously difficult to wrap your head around.
