Congratulations

Monitoring Machine Learning in Python

Hakim Elakhrass

Co-founder and CEO of NannyML

Chapter 1 recap

  • Fundamentals of NannyML library
  • Data preparation process for NYC Green Taxi dataset
  • Learn how to estimate the performance using CBPE and DLE
Monitoring Machine Learning in Python

Chapter 2 recap

  • Measuring performance when ground truth is available
  • Learning how to filter, plot and convert results dataframe format
  • Understanding chunking and thresholds
  • Calculating and estimating model's business value

The image shows monitoring workflow with highlighted performance monitoring.

Monitoring Machine Learning in Python

Chapter 3 recap

  • Performing multivariate drift detection
  • Testing various univariate drift detection methods
  • Using data quality checks calculators
  • Understanding various issue resolution methods

The image shows monitoring workflow with highlighted automated root cause analysis and issue resolution.

Monitoring Machine Learning in Python

What's next?

  • Explore NannyML's blog for tutorials
  • Refer to NannyML's documentation for more information
  • Consider taking additional courses on machine learning model lifecycle and MLOps
  • Experiment with practical projects and incorporate NannyML
Monitoring Machine Learning in Python

Thank you!

Monitoring Machine Learning in Python

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