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Introduction to Machine Learning
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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What is Machine Learning Model Testing?
For monitoring the ML model after deployment, various steps occur throughout the whole ML pipeline. 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 one is 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 continuously need to be running. So that company can ensure that its model provides continuous value even after some time.
Why is testing and monitoring importance?
Machine learning model testing is an underdeveloped area of exploration compared to software testing. Many organizations need help understanding what to test to check the model.
The importance of testing and monitoring of model is as follows:
Representativeness of training data
Machine learning models depend not only on code but also on data. So data that is getting used to build a model needs to be assessed to understand the model's 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.
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.
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.
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 its production life 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.
The procedure of evaluating the wellness of models performance against the real data. Click to explore about our, Machine learning Model Validation Testing
What are the best methods for testing and monitoring?
The problems stated above will require different assessments. Like 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 is getting 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.
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.
Non-zero values and Missing data can be evaluated with the chi-squared test. The chi-squared test will help 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.
Why is there a need to do these tests? This test shows how similar the live data and training data are. This test will give an idea to an organization of how biased the training data is and how the market has changed after the training data was gathered.
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Why to updating the ML model?
After monitoring the model, identifying significant concept drift, and realizing that it needs improvement, it's time to deploy the updated model. This process is completely a part of the lifecycle of the ML model. So here, the best practice is to make the process 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 can evaluate performance.
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. But in some scenarios where data get 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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What are best model monitoring tools?
The below mentioned are the best Machine Learning model monitoring tools:
Neptune is metadata that stores for MLOps built for productions and research teams that run a large number of experiments.
When it comes to monitoring ML models, organizations use them for
- Hardware metrics display
- model testing, evaluation, and training.
- For log performance metrics.
It has a flexible structure that allows organizations to organize production and training metadata as they want to. One can also build dashboards that will display the hardware metrics and performance one wants to see for better organizing model monitoring information.
Arize AI is a Machine Learning model monitoring platform that is able of helping to troubleshoot production AI and boost the project's observability.
Arize AI has the following features:
- Automate monitoring
- Simple integration
- pre-launch validation.
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 model and data issues using built-in tools.
- Use popular frameworks and libraries, such as sagemaker, MLflow, and Spark.
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 is:
- Regression Model performance
- Numerical Target Drift
- Classification model performance
- Data Drift
- Categorical Target Drift
Monitoring and testing ML models is an emerging field that is being developed. There are various methods for testing and monitoring data and models in production, identifying their root causes, and detecting potential issues early on. Using all the methods will help businesses to build software that will function as expected.
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