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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.
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Now

0-2 years
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What's changing

  • 1Manual deployment of models as APIs
  • 2Custom scripts for monitoring
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Implications

  • High reliance on 'unicorn' engineers
  • Slow deployment cycles

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