How To Monitor Your Machine Learning ML Models

What is a Machine Learning Model?

Machine Learning (ML) models are data sets that have been taught to identify specific occurrences. The trained model may then generate inferences and predictions about data it has never seen before. Machine learning requires reliable outcomes from an automated decision or assessment process. It is also challenging, if possible, to provide a clear description of the answer or the criteria used to make a choice.

Why is it required to monitor Machine Learning models in production?

The success of a machine learning model may be tracked both while it is being trained and while it is being used in production. The predictions of a machine learning model are compared with the known values ​​of the dependent variable in a dataset, and ML engineers establish model performance metrics such as accuracy, F1 score, Recall, etc. There is typically a disparity between the training data used to develop a model and the live, ever-changing data in a production setting. Due to this, a production model’s performance inevitably declines with time. For this reason, it is essential to keep close tabs on these indicators to boost the performance of your models.

Monitoring Machine Learning models in production is required to:

  • Identify production issues with your model and the system providing support for your model before they have a material impact on your business.
  • Triage and debug running models or the inputs and systems that support them in production.
  • Guarantee that their findings and forecasts can be explained and published
  • Ensure proper model governance so that the mechanism by which the model makes its predictions is made clear to all interested parties.
  • Prepare a plan for enhancing and sustaining the model during production.

Why is model monitoring hard?

Machine learning (ML) models are intricate, and keeping tabs on them is no easy feat. Due to the stochastic nature of ML models’ outputs, defining a mistake can take time and effort. Furthermore, calculating the evaluation metrics on real-world data might be easier if labels are available.

Below are some of the main reasons that make it difficult to monitor the model:

  • Changes in input data distribution or the introduction or removal of features might cause a difference in the elements used to train a model. Models with non-deterministic (unpredictable) behavior are more challenging to troubleshoot when their behavior changes, especially if the model is data-dependent.
  • As ML systems improve, many engineering teams conduct numerous ingestion and feature engineering jobs, establishing separate pipelines. When the model gives the wrong output, it might not be easy to find the cause and troubleshoot each pipeline.
  • Since these configurations often dictate model versions and hyper-parameters, even slight errors in the system design might result in the ML system behaving differently.

How to monitor Machine learning models?

Providing a feedback loop from the production environment into the model-building process is one of the main focuses of ML monitoring. Because of this, machine learning models may automatically refine themselves by updating or reusing previously trained models. Let’s begin by taking a look at some of the factors that might be kept in mind while keeping tabs on ML models:

  • Identify data distribution changes – performance might suffer when the model receives new data that is considerably different from the original training data.
  • Identify training-serving skew – despite comprehensive testing and validation during development; a model could not yield good results in production.
  • Identify concept or model drift – when a model first performs well in production but worsens in performance over time, this signals drift.
  • Identify health concerns in pipelines – in certain circumstances, difficulties with models step from errors during automated processes in your pipeline.
  • Identify performance concerns – even good models might only fulfill end-user expectations if they are sluggish to respond.
  • Identify data quality concerns – monitoring may assist ensure that both production data and training data come from the same place and are processed in the same way.

ML model monitoring can be done in two ways:

1. Functional monitoring

Here, the focus is on keeping tabs on the model’s outputs, how they compare to the inputs, and any other activity occurring within the model as it learns in production. The system keeps an eye on everything from raw data to model output to forecasts.

2. Operational monitoring

In operational monitoring, the main is primarily monitoring the resources your model runs on (and runs in) in production and ensuring that they’re healthy. It includes monitoring resources such as pipeline health, system performance metrics (I/O, disc utilization, RAM and CPU consumption, traffic, stuff that operations people normally care about), and cost. It monitors system utilization and expense.

ML monitoring techniques/types

1. Feature Quality Monitoring

Three types of data integrity issues plague ML models in production: missing values, range violations, and type mismatches. In the event of a data error, the model will not terminate immediately with an error message, as this would be detrimental to both the user experience and the company’s security. Instead, the model makes predictions after being fed incorrect data, and it’s only sometimes clear that everything is wrong. These mistakes often go undetected and chip away at the model’s effectiveness over time unless further monitoring is performed.

The integrity and consistency of the model’s data should be monitored constantly. There has to be a warning mechanism to identify low-quality features as soon as possible so they can be fixed.

2. Drift Monitoring

When a model is put into production, it might experience data drift if the data it sees deviates too far from the data it was trained. The world is never static, so some sway is to be expected. No matter the cause of the drift, it is essential to spot it as soon as possible to keep the model accurate and limit the damage to the business. Data drift is a useful surrogate statistic in these situations. The model’s performance is likely to deteriorate even if you can’t yet see it happening if your data is drifting.

In addition, keeping an eye on data drift ensures that you always know where your data stands. It is crucial for model iteration and feature discovery and may inform non-ML business choices.

3. Unstructured Model Monitoring

A rising number of firms construct natural language processing (NLP) and computer vision (CV) models comprising unstructured data, including text and pictures. These models are related to the development of new products and services while also streamlining internal procedures.

The need for a monitoring system for optimal machine learning performance has increased as the use of unstructured ML models spreads across all sectors of the economy.

4. Granular Monitoring

To acquire more detailed insights into the model’s performance, it is vital to regularly analyze it on individual data slices and investigate per-class performance. The best way to ensure your model continues to function at its best is to periodically check for and address any problems it may be experiencing. Additionally, underperforming slices may be automatically identified for further analysis and model improvement.

5. Model and Prediction Monitoring

Constantly assessing performance on real-world data is the simplest approach to keeping an eye on an ML model. Important shifts in metrics like accuracy, precision, or F1 can be signaled via customizable triggers. One can use model monitoring tools to automate this process if one wants to save time and minimize stress on the data science team.

Summing up

Production machine learning systems must have their data quality monitored for protection. Many problems with a model can be found in the data before they affect the model’s real performance. It’s a simple diagnostic test, like checking latency or memory use. Both human and machine-generated data depend critically on it. The two systems are prone to different kinds of mistakes. Monitoring data might also indicate data sources that have been abandoned or untrustworthy. ML monitoring is an emerging subject that has yet to be fully explored. In this article, we learned about many techniques for keeping an eye on the ML model and data to spot any problems and determine their origin.


Sponsored Content: Thanks to the Fiddler AI team for the thought leadership/ Educational article above. Fiddler AI has supported and sponsored this content.


Dhanshree

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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone’s life easy.


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