In this guide, I want to explain both the how and why of our approach, and hopefully, give you a better way to use A/B testing machine for test your models in production.
There is (rightfully) quite a bit of emphasis on testing and optimizing models pre-deployment in the machine learning ecosystem, with meta machine learning platforms like Comet becoming a standard part of the data science stack.
There has been less of an emphasis, however, on testing and optimizing models post-deployment, at least as far as tooling is concerned. This dearth of tooling has forced many to build extra in-house infrastructure, adding yet another bottleneck to deploying to production.
We’ve spent a lot of time thinking about A/B testing deployed models in Cortex, our open source ML deployment platform. After several iterations, we’ve built a set of features that make it easy to conduct scalable, automated A/B tests of deployed models. In this guide, I want to explain both the how and why of our approach, and hopefully, give you a better way to test your models in production.
DevOps and Cloud computing are joined at the hip, now that fact is well appreciated by the organizations that engaged in SaaS cloud and developed applications in the Cloud. During the COVID crisis period, most of the organizations have started using cloud computing services and implementing a cloud-first strategy to establish their remote operations. Similarly, the extended DevOps strategy will make the development process more agile with automated test cases.
What is DevOps? How are organizations transitioning to DevOps? Is it possible for organizations to shift to enterprise DevOps? Read more to find out!
What is DevOps? What are the goals it helps achieves? What are its benefits? This article has answers!
The year 2020 has arrived, and its arrival brings a lot of innovations and transformations in the Information and Technology (IT) sector to DevOps technologies.
DevOps is supposed to help streamline the process of taking code changes and getting them to production for users to enjoy. But what exactly does it mean for the process to be "streamlined"? One way to answer this is to start measuring metrics.