Chavez Harris

Machine learning models are the core building blocks of artificial intelligence. As of this writing, a popular AI chatbot circulating in the media and tech industry is ChatGPT. It uses several large generative language models under the hood and can perform tasks that some might describe as super-human.

This advancement in AI showcases the potential of machine learning models and their transformative impact. With the number of machine learning libraries available on the internet, you can even develop your custom models. What's even better is that you can decouple the features of your model and control how they behave using feature flags.

The primary goal of software developers is to ensure user satisfaction with the features or updates they introduce. However, achieving this goal can be challenging without the right release strategy. The question often asked, then, is, "How can developers be certain that a new update or feature delivers optimal results to end users?"

Two strategies that can be employed to address this concern are staged rollouts and canary releases. These strategies can be implemented using feature flags, and in this article, we explore how ConfigCat, a popular feature flag provider, can be used to perform staged rollouts and canary releases.

For a long time, it was normal to initially release a new feature or update into a test environment. If the feature passed, it was then released to the production environment. While this approach was highly respected and beneficial, it introduced more complexity into software development workflows, and releases took longer to reach end-users. Fortunately, with a mechanism known as feature flagging, you can deploy directly to production and ship releases faster while maintaining reliability.

Docker is a platform that enables developers to build apps and run them in mini virtual machines called containers. As a result, developers can just focus on writing code without needing to set up or configure an environment for running that code. Docker also allows easy application sharing because its environment is abstracted away from the host machine. This allows the containerized application to run on any host machine Docker is installed on. Developers can extend the functionality of Docker's desktop application with extensions. But the goodness doesn't stop there. You can use feature flags to control smaller feature components of these extensions without rebuilding and updating them.

As a front-end developer, I spend most of my time writing code and developing front-end applications. Several months ago, I wondered what it would be like to run a tech startup. It turns out that I spend my spare time writing blog articles for such a company. Meet ConfigCat, a thriving tech startup that offers a cloud-hosted feature flagging solution to other tech companies.

Most importantly, I was curious to know how the company was created and the secret behind its success, as well as how they are able to handle high-end user demands while delivering a seamless feature flagging solution. To answer these questions, I decided to conduct an online interview with the core engineering team to satisfy my curiosity and to share what I found with you, the reader.

One of the most important technological breakthroughs of the century has been the internet — a digital network that makes the rest of the world feel like our next-door neighbor. Within the realm of the Internet, a recent technology known as cloud computing has paved the way for software developers to rent and manage remote servers in the cloud for hosting their applications. A smaller component of this technology is called Function as a Service, abbreviated as FaaS. FaaS removes the complexity of managing a full-blown backend server, enabling developers to focus solely on writing and executing the necessary functions required to run their applications.

When FaaS and feature flags are combined, you can toggle individual functions or code blocks in those functions on or off without touching its code. Let's take a closer look.

Feature flags are essential for effective feature release and management. Using them, we can control what features end users can see and which should remain hidden. Feature flagging allows developers to plan, launch and test new features remotely without editing code. While these benefits are fantastic, what about code testing? Having some methods in place for testing the integration of feature flags in our code can increase the likelihood of smooth feature integrations.

According to an article published by CNET, the growth of the gaming industry is expected to increase. Due to this, new game titles are on the rise as greater demands are placed on gaming companies to remain competitive by keeping their users engaged with new features and updates. With the proper feature flagging mechanism, new features and updates can be effectively managed and released to users.

Ever since the dawn of feature releases, feature flags have become the de facto standard for managing and controlling features in software applications. Many software development methodologies these days such as agile, are heavily focused on releasing continuous updates and features. In addition, a few companies have based their entire business around serving clients a cloud-based feature flagging solution. But in limited bandwidth situations or when you need to optimize the performance of your client-facing applications making API requests may not be ideal. This can be handled by implementing a process called caching with the help of a popular tool called Redis.

It can be time-consuming to create and manage the infrastructure that drives your software applications as they grow and become larger. Also, what about ongoing updates and releases of new features? Luckily, there is a solution to this problem in the form of a tool designed by Hashicorp called Terraform. This allows us to define our infrastructure in a central configuration file without having to create it on every provider platform we use.