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The MLOps Mindset

Updated May 13, 2023 ·

Overview​

MLOps (Machine Learning Operations) helps teams manage ML models in production. It ensures smooth deployment, monitoring, and scaling.

  • Collaboration between data scientists and operations teams
  • Testing models before deployment
  • Scaling to handle production workloads

By following MLOps practices, models stay reliable and efficient.

Running ML Experiments​

ML experiments test different models to find the best one.

  • Train models on various datasets
  • Evaluate accuracy and reliability
  • Select the best-performing model

This process is key to improving machine learning projects.

From Experiments to Production​

A model moves to production when it is tested and validated.

  • Document the model and its parameters
  • Test with different datasets
  • Monitor performance in real-world settings

Once validated, it should be deployed in a secure, scalable environment.

Why ML Experiments Fail​

Many ML experiments don’t reach production due to common issues.

  • Unclear goals make success hard to measure
  • Poor data quality leads to unreliable results
  • Overly complex models are hard to deploy
  • Overfitting/underfitting affects accuracy

Addressing these problems improves success rates.

Technical Debt​

Technical debt happens when rushed code causes future issues.

  • Unvalidated code leads to bugs
  • Outdated documentation makes debugging harder
  • Quick fixes cause long-term problems

Prioritizing quality and proper testing prevents technical debt.