Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, there’s been a significant democratization of model creation, with more developers and organizations engaging in ML model development than ever before.

If you deploy container-based services in Kubernetes, chances are you’re also using infrastructure-as-code to help automate the provisioning and maintenance of the cloud environments where your applications will run. Up until recently, Terraform was “the name” in infrastructure-as-code.

What it means to be “enterprise grade” has changed. In software development, the size of new artifacts and the pace of development has increased dramatically. Developers are now releasing new components daily, if not multiple times a day. With containerization, and now AI/ML models, new pieces of software can be multiple GBs or larger.

It’s a scenario many developers know all too well: a configuration works flawlessly for one team member but doesn’t work for you. Starting a new job brings with it the excitement of fresh challenges and opportunities. However, it also entails the often painful task of setting up your development environment—a process that can be both time-consuming and complex. This setup phase involves installing dependencies, configuring projects, and troubleshooting issues that might arise.