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1 | | -# Understanding Machine Learning Monitoring |
| 1 | +# Understanding machine learning monitoring |
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3 | 3 | Machine learning (ML) monitoring is a crucial process for ensuring the performance and reliability of ML models and systems. It involves tracking metrics, identifying issues, and improving overall performance. In this article, we will explore the key aspects of ML monitoring and its importance in today's data-driven world. |
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5 | | -## What is Machine Learning Monitoring? |
| 5 | +## What is machine learning monitoring? |
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7 | 7 | ML monitoring is the process of tracking the performance and behavior of ML models and systems. This includes monitoring metrics such as accuracy, precision, recall, and model performance over time. By monitoring these metrics, organizations can identify when a model is performing poorly or behaving unexpectedly and take action to correct it. Additionally, ML monitoring can be used to track the performance of different models and compare them to identify which model is performing best. |
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9 | | -## Detecting Poor Performance |
| 9 | +## Detecting poor performance |
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11 | 11 | One of the key aspects of ML monitoring is being able to detect when a model is performing poorly or behaving unexpectedly. This can be done by setting up alerts on certain metrics or by monitoring for unexpected changes in the model's behavior. For example, if a model's accuracy suddenly drops, an alert can be triggered to notify the team responsible for maintaining the model. This allows them to quickly investigate and address the issue, minimizing the impact on the organization's operations. |
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13 | 13 | ## Monitoring data and concept drift |
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15 | 15 | Another important aspect of ML monitoring is the ability to detect and address data and concept drifting. Data drifting refers to the gradual change in the distribution or characteristics of the input data over time. As the data changes, the model's performance may decrease, which is why it is important to detect data drifting and retrain the model with new data. Concept drifting, on the other hand, happens when the statistical properties of the target variable, which the model is trying to predict, change over time. This can happen due to various reasons such as changes in the underlying data distribution, overfitting, or degradation of the model's parameters. To address these issues, beside drift detection, organizations can use techniques such as monitoring of the performance of the model over time, retraining the model regularly etc. |
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17 | | -## Monitoring Input Data |
| 17 | +## Monitoring input data |
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19 | 19 | Another important aspect of ML monitoring is being able to track the input data that is being fed into the model. This can be used to identify any issues with the data, such as missing values or outliers, and to ensure that the data is being processed correctly. This helps to ensure that the model is making accurate predictions and that the data is being used effectively. |
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21 | | -## Monitoring Infrastructure and Resources |
| 21 | +## Monitoring infrastructure and resources |
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23 | 23 | Finally, it is important to monitor the infrastructure and resources that the ML models are running on. This includes monitoring things like CPU and memory usage, disk space, and network traffic. This helps to ensure that the models have the resources they need to perform well and to identify any potential bottlenecks that may be impacting performance. By monitoring the infrastructure, organizations can ensure that their models are running smoothly and can make adjustments as needed. |
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