What tools do ML engineers actually use day-to-day (besides training models)?
Analysis
This Reddit post from r/MachineLearning asks about the essential tools and libraries for ML engineers beyond model training. It highlights the importance of data cleaning, feature pipelines, deployment, monitoring, and maintenance. The user mentions pandas and SQL for data cleaning, and Kubernetes, AWS, FastAPI/Flask for deployment, seeking validation and additional suggestions. The question reflects a common understanding that a significant portion of an ML engineer's work involves tasks beyond model building itself. The responses to this post would likely provide valuable insights into the practical skills and tools needed in the field.
Key Takeaways
- •ML engineering involves more than just model training.
- •Data cleaning and feature engineering are crucial aspects.
- •Deployment and monitoring tools are essential for production.
“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.”