MLE Career Pivot: Certifications vs. Practical Projects for Data Scientists
Published:Jan 4, 2026 10:26
•1 min read
•r/learnmachinelearning
Analysis
This post highlights a common dilemma for experienced data scientists transitioning to machine learning engineering: balancing theoretical knowledge (certifications) with practical application (projects). The value of each depends heavily on the specific role and company, but demonstrable skills often outweigh certifications in competitive environments. The discussion also underscores the growing demand for MLE skills and the need for data scientists to upskill in DevOps and cloud technologies.
Key Takeaways
- •Experienced data scientists are seeking to transition into Machine Learning Engineering roles.
- •The AWS Certified Machine Learning Engineer - Associate certification is a popular option for upskilling.
- •There is debate on whether certifications or practical projects are more valuable to recruiters.
Reference
“Is it a better investment of time to study specifically for the certification, or should I ignore the exam and focus entirely on building projects?”