Hey everyone! I recently landed a job as a Machine Learning Engineer, but my background is in Mechatronics and Robotics. I've done some practical work in ML development for industrial applications, so I'm familiar with building and training ML models. However, I don't have much experience in software engineering. I'm looking for structured resources or a roadmap to help me understand the fundamentals and terminology of software engineering and DevOps, including concepts like CI/CD, Docker, and Kubernetes. Any recommendations would be greatly appreciated!
1 Answer
It's interesting to note that machine learning, software engineering, and DevOps each have their distinct roles. If you're mainly hired for ML, your employer might prefer you to focus on that specific area. Just curious, how did you land an ML position without a solid programming background?
Actually, I have strong Python programming skills and I'm well-versed in Git along with various ML topics. My goal now is to get a grip on DevOps essentials to properly deploy my models, especially MLOps aspects.