Machine Learning Model Deployment Testing | A Quick Guide

Machine Learning Overview

For many years, developers and businesses have understood how important it is to test the software before deployment. Before it can interface with the customers, the business wants its software to function as expected. With the increasing demand for ML, it is essential to expect the ML model deployed into production to be tested correctly.

After implementing the previous steps of the Machine Learning Pipeline, which are collecting the correct data, data preprocessing, feature engineering, feature selection, training, and deployment to production, the last step is testing and monitoring the model predictions and model performance. When testing these models, many other problems can occur once the live data enters the model.

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Understanding ML Model Testing

Various steps occur throughout the whole ML pipeline to monitor the ML model after deployment. The diagram below shows an ML system's end-to-end process, showing when different types of methods are needed for testing. The above diagram shows how model testing is implemented throughout the model's development process, like ML infrastructure tests, quality tests, and model performance tests. Once satisfied with the testing results, prediction monitoring companies can fully deploy the models to the customers, but this does not mean testing is completed here. The monitoring and testing processes need to be running continuously so that the company can ensure that its model provides continuous value even after some time.

  Advantages of Testing & Monitoring

Machine learning model testing is an underdeveloped area of exploration compared to software testing. Many organizations need help to understand what to test to check the model. The importance of testing and monitoring the model is as follows:

1. Representativeness of Training Data
   Machine learning models depend not only on code but also on data. So, the data used to build a model needs to be assessed to understand its feasibility in the real world. If the data on which the model is trained does not present the actual data well, then the model will not provide the business value during deployment.
2. Data Dependencies
   Data dependencies need to be monitored. If there is a data outage, one needs to pick that up immediately. Otherwise, models will continue to serve customers without conveniently accounting for the missing data. Data that one gets from third parties will only sometimes be available.
3. Feature Dependencies
   Feature dependencies need to be identified. When building a model, one must check whether features change over time. Sometimes, other teams within an organization create a feature; after that, there needs to be more alignment regarding what the features represent. So, identifying all the feature dependencies is essential.
4. Model Performance Drift
   Model performance drift also needs to be monitored. A starting model may perform well, but its performance deteriorates over time. An organization must check the model's accuracy throughout production to know if it falls below a predefined standard. If a business follows this step, it can identify why the model is worsening and how it can improve.
   
   
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Best ML Model Testing Methods

The problems stated above will require different assessments, such as different types of skew tests, live data checks, performance monitoring, and model prediction.
Here is the method for testing and monitoring the model.

Live Data Checks

Live data checks are used to check if one gets the data the same as expected in the live environment. One needs to monitor the data to ensure the model is working. These tests involve checking how input for variables matches what the model expects.

Skew Tests

This test will help to give an idea of how representative the used training data is of the live data. One of the simplest and most common forms of this test involves monitoring the missing data in the live data compared to the training data. The next concern is the percentage of non-zero values. The chi-squared test can evaluate non-zero values and missing data. It helps determine if two different proportions are similar. Proportions could be mean, like the proportion of missing values in the data on which the model is trained compared to the proportion of missing data in the live data.

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Why is it essential to update the ML model?

After monitoring the model, identifying significant concept drift, and realizing it needs improvement, it's time to deploy the updated model. This process is part of the ML model's lifecycle, so the best practice is to make it as smooth as possible.

A/B testing for ML models. Using A/B testing, one can evaluate whether the newer model performs better in scenarios. Additionally, A/B testing can help avoid issues while deploying a new model. So, one can start by directing some small percentage of the new model and evaluate performance.

Automated Retraining

Once a steady ML model is deployed and one has to go through the process of deploying and retraining the model again, it is time to automate it. However, in some scenarios where data gets changed quickly, it would be riskier for the online learning approach. In this case, the model gets updated whenever new examples are available.

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Best ML Model Testing Tools

The below-mentioned are the best machine learning tools:

Neptune

Neptune is metadata that stores MLOps built for productions and research teams that run many experiments.

When it comes to monitoring ML models, organizations use them for

• Hardware metrics display
• model testing, evaluation, and training.
• For log performance metrics.

Its flexible structure allows organizations to organize production and training metadata as they want. Users can also build dashboards that display the hardware metrics and performance they want to see to better organize model monitoring information.

Arize AI

Arize AI is a machine learning model monitoring platform that can help troubleshoot production AI and boost the project's observability.

Arize AI has the following features:

• Automate monitoring
• Simple integration
• pre-launch validation.

WhyLabs

WhyLabs is an observability and model monitoring tool that helps an organization monitor ML applications and data pipelines. This tool helps to:

• Detect model performance and successfully identify issues in the model.
• Debug models and data issues using built-in tools.
• Use popular frameworks and libraries, such as PageMaker, MLflow, and Spark.

Evidently

It is an open-source ML model monitoring system. This tool helps to analyze ML models during validation and production monitoring. Six reports are available in this tool, which are:

• Regression Model performance
• Numerical Target Drift
• Classification model performance
• Data Drift
• Categorical Target Drift

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Next Steps for Testing Model Deployment

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