The MLOps landscape is complex and hard to navigate. If the wrong decisions are made while creating your technology stack early on, it leads to complexity and technical debt as you progress through the stages of MLOps maturity. ZenML allows you to define your ML workflows in a vendor- and tool- agnostic manner, so that you can write your code knowing that you can switch out ML components easily as and when it is needed. Read more our our blog.

At a high level, an orchestrator in MLOps is a tool that enables developers to schedule, monitor, and manage workflows. ZenML is not an orchestrator, but rather is a tool that lets you author pipelines that can be run on multiple orchestration systems. There are standard orchestrators that ZenML supports out-of-the-box, but you are encouraged to write your own orchestrator in order to gain more control as to exactly how your pipelines are executed. ZenML not only allows you to easily swap our orchestrators, but also then focuses on adding value within the pipeline itself, with features such as model deployment, metadata tracking, and management of the configuration of your MLOps stack.

There are two sides to this question. ZenML itself can be deployed as a service, which centrally tracks everything related to pipelines and stacks. However, ZenML also often requires additional infrastructure for various stacks. The ZenML MLStacks repository contains Terraform scripts to quickly get you started with these stacks. These recipes can also be called directly with ZenML with zenml stack recipe deploy . Read more here.

You need a ZenML pipeline because it allows ML engineers to take ownership of the entire ML lifecycle end-to-end. Adopting ZenML means fewer handover points and more visibility on what is happening in your organization. ZenML also provides better reproducibility and allows you to develop production-ready code.

ZenML is built for data scientists, ML engineers, and MLOps platform engineers. It gives data scientists the freedom to fully focus on modeling and experimentation while writing code that is production-ready from the beginning. ZenML allows developers to develop ML models in any environment using their favorite tools and easily switch to a production environment once they are satisfied with the results.