What tools do ML engineers actually use day-to-day (besides training models)?
Published:Dec 27, 2025 20:00
•1 min read
•r/learnmachinelearning
Analysis
This Reddit post from r/learnmachinelearning highlights a common misconception about the role of ML engineers. It correctly points out that model training is only a small part of the job. The post seeks advice on essential tools for data cleaning, feature engineering, deployment, monitoring, and maintenance. The mentioned tools like Pandas, SQL, Kubernetes, AWS, FastAPI/Flask are indeed important, but the discussion could benefit from including tools for model monitoring (e.g., Evidently AI, Arize AI), CI/CD pipelines (e.g., Jenkins, GitLab CI), and data versioning (e.g., DVC). The post serves as a good starting point for aspiring ML engineers to understand the breadth of skills required beyond model building.
Key Takeaways
- •ML engineering involves much more than just model training.
- •Data cleaning and feature engineering are crucial aspects of the role.
- •Deployment, monitoring, and maintenance are essential for production ML systems.
Reference
“So I’ve been hearing that most of your job as an ML engineer isn't model building but rather data cleaning, feature pipelines, deployment, monitoring, maintenance, etc.”