ML - data drift

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What does data drift refer to in the context of machine learning models?

A change in the statistical properties and characteristics of the input data (correct)

A change in the hardware on which the model is deployed

A change in the output predictions of the model

A change in the machine learning algorithm used

How does data drift affect a machine learning model's performance?

It can lead to a decline in the model's performance (correct)

It has no impact on the model

It improves the model's accuracy

It speeds up the model's training process

Why is it important to monitor and address data drift in production ML models?

To keep the model's performance accurate over time (correct)

To ensure the model only encounters training data

To prevent the model from being trained

To increase the speed of the model's predictions

What can happen if a machine learning model faces data drift and is not adapted accordingly?

The model's performance may decrease Signup and view all the answers

What is the main concern addressed in the text regarding machine learning models?

Data drift Signup and view all the answers

In the retail chain example, what caused a significant shift in sales channels?

Marketing campaign for the mobile app Signup and view all the answers

What is the difference between data drift and concept drift?

Data drift involves changes in data distribution, while concept drift involves changes in relationships between input and target variables. Signup and view all the answers

How can prediction drift be best described?

Distribution shift in the model outputs. Signup and view all the answers

In what scenario could prediction drift be an indication of model issues?

If the model starts predicting outcomes with higher frequency. Signup and view all the answers

What is NOT a term related to data drift mentioned in the text?

Prediction skew Signup and view all the answers

What can cause data drift but not concept drift?

Average basket size per channel remains consistent. Signup and view all the answers

Which factor does concept drift primarily involve?

Shifts in relationships between input and target variables. Signup and view all the answers

What can prediction drift signal beyond changes in environment?

Issues with training data quality. Signup and view all the answers

What is the primary difference between data drift and prediction drift?

Data drift involves shifts in input feature distributions, whereas prediction drift refers to shifts in model outputs. Signup and view all the answers

What kind of shift can signal issues with model quality according to the text?

Shift towards more frequent fraud predictions by a fraud detection model. Signup and view all the answers

What is one of the methods mentioned in the text for early monitoring of model performance?

Tracking data distribution drift Signup and view all the answers

What issue can occur due to a significant time gap between making a prediction and receiving feedback?

Feedback delay Signup and view all the answers

In which scenario might it be challenging to definitively label a user transaction as fraudulent or legitimate?

Payment fraud detection Signup and view all the answers

Why are ground truth labels important in evaluating model quality?

To evaluate the model quality accurately Signup and view all the answers

What technique is useful for model troubleshooting and debugging?

Data drift analysis Signup and view all the answers

In which situation might data drift analysis not be used as an alerting signal?

Model debugging and troubleshooting Signup and view all the answers

What is a common way to compare two distributions, mentioned in the text?

Looking at key summary statistics Signup and view all the answers

When comparing summary statistics, what issue can arise if monitoring many features at once?

"Noisy" observations due to multiple comparisons Signup and view all the answers

"How 'different' is different enough?" refers to which aspect of the text?

"Detecting a change in distributions" Signup and view all the answers

What is a common industry approach to retrain machine learning models when facing data drift?

Retrain the model using old and new data Signup and view all the answers

When observing unnecessary data drift alerts, what adjustment might you make to the sensitivity of drift detection methods?

Decrease the sensitivity Signup and view all the answers

What could happen if a machine learning model's predictions are adversely affected by drift?

The model's operation might need to be temporarily halted Signup and view all the answers

What is one way to adjust machine learning models to be more resilient to data shifts without taking a reactive approach?

Review historical variability of features and filter out ones with significant drifts Signup and view all the answers

Which action might be taken if retraining a machine learning model is not feasible due to a lack of new labels for model updates?

Consider process interventions Signup and view all the answers

What could be a consequence of continuing to use a machine learning model without verifying that the data is valid and complete?

Potential false positives in predictions Signup and view all the answers

What is a recommended rule of thumb when observing data drift in machine learning models related to alerting?

Alert only to drift in top model features Signup and view all the answers

When it comes to updating machine learning models due to a true data drift, what specific actions might be necessary?

Develop a completely new approach from scratch Signup and view all the answers

What could be a consequence of neglecting to adjust the sensitivity of drift detection methods when unnecessary alerts are observed?

Continued unnecessary alerts causing disruptions. Signup and view all the answers

How can machine learning models be designed to be more resilient to data shifts without reacting to changes?

Apply feature selection based on historical variability. Signup and view all the answers

What might happen if a machine learning model continues operating without considering data quality verification?

Elevated risk of generating false positives. Signup and view all the answers

What action might be taken if retraining a machine learning model isn't viable due to missing labels for updates?

Halt the operation of the model temporarily. Signup and view all the answers

What is the difference between data drift and training-serving skew?

Data drift refers to gradual changes in input data distributions, while training-serving skew refers to immediate post-deployment discrepancies. Signup and view all the answers

What can trigger a training-serving skew?

Mismatch between the data the model was trained on and the data it encounters in production. Signup and view all the answers

How do you distinguish data quality issues from data drift?

Data quality issues involve corrupted and incomplete data, while data drift involves changes in otherwise correct and valid data distributions. Signup and view all the answers

In which situation can you encounter a training-serving skew?

If there's a mismatch between the model's input training data and production data. Signup and view all the answers

What is the common similarity between data drift and prediction drift?

Both are useful techniques for production model monitoring without ground truth. Signup and view all the answers

When might you face a training-serving skew immediately after model deployment?

If there's a mismatch between the model's training data features and production feature availability. Signup and view all the answers

What does data drift refer to?

Gradual changes in input data distributions. Signup and view all the answers

What is the similarity between data quality issues and data drift?

Both can lead to model quality drops Signup and view all the answers

What is the main implication of a training-serving skew on model performance?

The model might not perform well if it lacks important attributes trained on. Signup and view all the answers

What is the primary goal of drift detection?

Decide if the model still performs as expected Signup and view all the answers

How do outliers differ from data drift?

Drift helps monitor model inputs while outliers do not Signup and view all the answers

What can signal a change in the model environment without ground truth?

Both data drift and prediction drift. Signup and view all the answers

Why is tracking data distribution drift considered important?

To maintain production ML model quality Signup and view all the answers

What actions can help differentiate between data quality issues and data drift?

First verify completeness of the data, then check for distribution shifts. Signup and view all the answers

What is a key reason for ongoing model maintenance in machine learning systems?

To keep models updated due to changing real-world data Signup and view all the answers

What is one way to detect a training-serving skew?

When there's a mismatch between the features available during training and those available during production. Signup and view all the answers

How does detecting outliers differ from detecting data drift?

Drift detection focuses on individual unusual inputs in the data Signup and view all the answers

What is a common feature of data drift and outliers existing independently?

Detection methods for both should be designed differently Signup and view all the answers

How does outlier detection differ from drift detection?

Outlier detectors should be robust to some outliers, while drift detectors should be sensitive enough to catch individual anomalies. Signup and view all the answers

What is a key purpose of outlier detection?

Identify individual objects in the data that look different from others Signup and view all the answers

What is one drawback of using statistical tests for data drift detection?

Statistical tests may be overly sensitive with large datasets. Signup and view all the answers

When is it recommended to use distance metrics for detecting data drift?

When dealing with a large dataset where statistical tests may be too sensitive. Signup and view all the answers

What is the purpose of using rule-based checks for data drift?

As alerting heuristics to detect meaningful changes. Signup and view all the answers

Why might statistical significance not always imply practical significance in data drift detection?

The p-value might not accurately reflect the drift magnitude. Signup and view all the answers

Which distance metric is commonly used to understand the extent of drift in data?

Jensen-Shannon Divergence Signup and view all the answers

In what scenario are rule-based checks particularly useful for detecting data drift?

In industries like healthcare or education. Signup and view all the answers

Why might using statistical hypothesis testing for data drift be challenging?

Selecting the right test based on data distribution assumptions can be complex. Signup and view all the answers

What factor influences whether statistical tests or distance metrics are more suitable for data drift detection?

The size of the dataset being analyzed. Signup and view all the answers