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Complete the Machine Learning Loop

Like CI/CD for DevOps, machine learning happens on a continuous development cycle. When it comes to MLOps, there are two sides to the loop: code and data. The machine learning loop challenges data scientists to approach their data as code – fully versioned, explainable, and clean.

What is the machine learning loop?

 When applying machine learning to real-world problems, it’s a necessity to separate hype from reality. And it’s hard to imagine this utopian future when I struggle daily with seemingly trivial failure points in NLP and Speech Recognition models. Whether it’s data drift, performance improvements, or even black swan events, you must monitor, diagnose, refine, and improve the model to keep up with an ever-changing world. The challenge is that your model is only half of the story: data science is the merger of two development cycles, your code and your data. 

This whitepaper covers: 

  • Improving your Machine Learning capabilities
  • The Code-Data loop
  • Troubleshooting your data
  • A Practical MLOps approach

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