Operations | Monitoring | ITSM | DevOps | Cloud

Data pipelines are the backbone of Machine Learning projects. They are responsible for collecting, storing, and processing the data that is used to train and deploy machine learning models. Without a data pipeline, it would be very difficult to manage the large amounts of data that are required for machine learning projects.

Most companies today have multiple cloud instances with multiple cloud service providers (CSPs) as well as an on-premises environment. It’s complex, but that doesn’t make it inherently wrong—there are usually good business reasons behind the decisions. It does, however, create management challenges.

As a software engineer, one of your goals is to ensure that your product can be accessed globally by your customers. It’s not enough that an app is bug-free and works flawlessly if it only works on localhost. Docker was introduced to solve the “it works on my machine” problem. For example, the particular version of a programming language a developer is using on Windows or MacOS may not be working on the hosting server.

Why do we want to eliminate trust? Isn’t trust a good thing that we should foster and grow? And shouldn’t computing platforms trust their end-users, and vice versa? The short answer is no. And I would argue that the very goal of system security has always been to reduce trust.