What is machine learning model deployment?

Machine learning model deployment is the process of integrating a trained machine learning model into a production environment to make predictions or decisions based on new data.

What is ML model monitoring?

ML model monitoring involves continuous tracking and evaluation of a deployed machine learning model's performance to ensure its accuracy and reliability over time.

How to monitor ML models?

Monitoring ML models involves tracking key performance metrics, data drift, and concept drift, while implementing alerting systems to detect issues proactively.

Data drift refers to the phenomenon where the statistical properties of input data used for machine learning models change over time, potentially impacting model performance.

What is concept drift?

Concept drift occurs when the relationship between features and the target variable in a machine learning model changes over time, affecting its accuracy.

What is the F1 score in machine learning?

The F1 score is a metric that balances precision and recall, providing a single measure of a model's performance in binary classification tasks.

The F1 score is a statistical measure used to evaluate the accuracy of a binary classification model, considering both precision and recall.

Navin Kumar Parthiban - Author of iTech

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Navin Kumar Parthiban

Data Masking and Data Anonymization for AI in Healthcare

Considering that AI and ML applications are increasingly becoming a part of business operations, an important question pops up that cannot be ignored. Can machine

October 26, 2023

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