Operations | Monitoring | ITSM | DevOps | Cloud

The days of taking an afternoon off from work to head over to the bank are long gone. Banks have rapidly digitized, offering the vast majority of their services to customers remotely. Now users can save, invest, borrow, and make other financial transactions from the comfort of their smartphone, wherever they are. Despite this consumer-facing digitization, many banks have clung to the status quo when it comes to their field operations.

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.

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.

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.