← Back to search
MLOps Engineer
Machine Learning OperationsAI Infrastructure Engineer
Yes, but the focus shifts from managing servers to managing data drift and model lifecycle reliability.
Key Takeaways
- 1Automates the deployment, scaling, and monitoring of machine learning models.
- 2Ensures data pipelines are reliable, secure, and reproducible for continuous training.
- 3Collaborates with Data Scientists to turn experimental code into production-ready software.
🚀
Now
0-2 years🔄
What's changing
- 1Manual deployment of models as APIs
- 2Custom scripts for monitoring
💡
Implications
- High reliance on 'unicorn' engineers
- Slow deployment cycles