Hello,
Recently, I've been entrusted with a significant project at my organization that requires a deeper understanding and implementation of MLOps principles and practices. The primary objective outlined for me is to leverage DVC (Data Version Control) to automate our ML pipelines. Furthermore, there's an emphasis on mastering pipelines & experiment automation, ensuring that our ML workflows are efficient, reproducible, and scalable.
Given the above context, I'm keen on comprehensively understanding and implementing these processes. However, I'm currently at an impasse, trying to ascertain the most logical and efficient path forward. Specifically, I'm seeking guidance on how to methodically and effectively approach each of these tasks.
Could you provide me with a structured breakdown or a roadmap on how to proceed with automating pipelines using DVC, handling experiment automation, and building automated pipelines in general?
Additionally, if there are any foundational prerequisites or best practices that I should be aware of before diving in, I'd greatly appreciate that insight.
I followed this resource but didn't get too much- https://www.cloudera.com/tutorials/building-automated-ml-pipelines-in-cml.html
Thank you for your time, and I eagerly await your guidance on this matter.
