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Pythonで学ぶ機械学習のモニタリング

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更新日 2025/05

Pythonで基本的な機械学習モニタリングシステムを構築するために必要な知識を一通り学べます

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PythonMachine Learning

3時間

11 ビデオ

38 演習

2,800 XP

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コース説明

前提条件

Monitoring Machine Learning Concepts

1

Data Preparation and Performance Estimation

In this chapter, you will be introduced to the NannyML library and its fundamental functions. Initially, you will learn the process of preparing raw data to create reference and analysis sets ready for production monitoring. As a practical example, you will investigate predicting the tip amount for taxi rides in New York. Toward the end of the chapter, you will also discover how to estimate the performance of the tip prediction model using NannyML.

What is NannyML?

Key features of NannyML

Load the dataset

Data preparation for NannyML

Reference or analysis period?

Loading and splitting the data

Creating reference and analysis set

Performance estimation

Specify the algorithm and problem type

Interpreting results

CBPE and DLE workflow

Performance estimation for tip prediction

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2

Monitoring Performance and Business Value

In this chapter, you will be introduced to realized performance calculators used when ground truth becomes available. You will learn about the more advanced methods for handling results, including filtering, plotting, converting them to data frames, chunking, and establishing custom thresholds. Lastly, you'll apply this knowledge to calculate the business value of a model trained on the hotel booking dataset.

When labels are available

When performance estimation is off

Comparing estimated and realized performance

Working with calculated and estimated results

Different chunking methods

Modifying the thresholds

Interacting with results

Business value calculation and estimation

Business value calculation

Drop in monetary value

Business calculation for hotel booking dataset

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3

Root Cause Analysis and Issue Resolution

Having detected the performance degradation in the hotel booking model, you will now learn how to identify the underlying issue causing it. In this chapter, you will be introduced to multivariate and univariate drift detection methods. You will also learn how to identify data quality issues and how to address the underlying problems you detect.

Multivariate drift detection

Identifying relevant drifts

Drift in hotel booking dataset

Univariate drift detection

Univariate drift detection for hotel booking dataset

Ranking the univariate results

Visualizing drifting features

Data quality and statistic checks

Data quality checks

Summary statistics

Issue resolution

What is the resolution?

Should you do nothing or not?

Implementing a monitoring workflow

Congratulations

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Pythonで学ぶ機械学習のモニタリング

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