The machine learning model life cycle doesn’t end after deployment. Model monitoring helps ensure the continuous monitoring of ML models which in turn impact business operations positively. Model monitoring also makes sure that the ML model is
constantly learning by studying the patterns in real-world data. This article discusses model monitoring and why it is needed for your business.
What is model monitoring?
Model monitoring is the process of tracking and observing machine learning model performance after deployment. It is essential for keeping deployed models accurate and for quality predictions. It also ensures that the ML model adapts to changing data and enables the ML model to generalize better on live data. Model monitoring is very essential in machine learning Operations (MLOps) because it helps ensure that the model is performing accurately. Model monitoring also integrates into the MLOps and DevOps lifecycle, it serves as a feedback mechanism and ensures model management.
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Key features of model monitoring tools
Many model monitoring tools help with ML monitoring and model management. Model monitoring tools also require no programming knowledge. These tools are adopted by teams and are widely used. The key features of these tools are listed below
- It should have an easy integration for model monitoring: Model monitoring tools should provide easy integration of model hub tools with model deployment tools in the ml lifecycle.
- It should provide alerting capabilities: This is another important feature of a model monitoring tool. Whenever there is any interference or error with the machine learning model and the modelops, these tools must alert operators. These alerts can help ensure that the ML model is immediately checked and this will ensure its continuous operation.
- Monitoring facilities: Model monitoring tools must provide monitoring capabilities, by ensuring that the machine learning model in production is efficiently being monitored.
- Should have a simple user interface and provide Application programming interfaces (APIs): Model monitoring tools should have a well-designed user interface with simple point and click icons. Most model monitoring tools also provide an application programming interface for developers so that they can integrate it with any programming language of their choice.
Six reasons why model monitoring is needed for your business
Businesses can derive a lot of benefits by adopting model monitoring as part of their operations. Some of these benefits are discussed below
1. Model monitoring helps build reliable ML models by eliminating generalization and ensuring the stability of prediction which in turn drives the business operation forward.
Model monitoring helps make sure accurate and healthy machine learning models are built by providing efficient monitoring of model parameters. For example, while training a machine learning model, several hyperparameters need to be tuned, a k-nearest neighbor algorithm (knn) will require that the right number of neighbors be chosen, also a decision tree classifier will require that the right number of maximum tree height be chosen, also choosing the learning rate of a deep learning neural network model. Model monitoring tools can help choose the right hyperparameters which in turn produce an accurate ML model. These ML models can help make predictions like the sales rate of a product in the next year, these will, in turn, drive the business operations forward.
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2. Model monitoring ensures business analysis and insights
Model monitoring tools can help with business analysis because model monitoring tools are driven by data. Before a machine learning model is trained, data needs to be collected from different sources and preprocessed. The preprocessing ensures that missing data, and outliers are properly handled, and encoding of categorical data is done. Data analysis can then be performed on this data, during which visualizations are conducted. This process helps discover business insights that help to understand the business situation. It also helps to discover potential problems in the business, and business analysis can then be performed to solve this problem and satisfy the customers.
3. Model monitoring ensures data integrity in business
Model monitoring helps ensure data integrity during the building of a machine learning model which in turn improves business operations. This is done by ensuring the overall completeness, accuracy, and consistency of data that is stored in the database and data warehouse. Businesses use these databases to store their data and it is accessed by different customers. The data stored in the data must be malware free and safe for use. Model monitoring ensures that they have good data quality else they will lose customers due to corrupt data.
4. Model monitoring eliminates business decision bias.
Model monitoring helps businesses make a generalized business decision that is favorable to all stakeholders and customers. Monitoring the ML models helps provide accurate decisions because of the large data the model was trained on. The larger the data the more generalized the ML model is. This then produces results that impact the business decision positively and eliminate the decision bias.
5. Model monitoring helps ensure continuous business operations.
Continuous business operations are the activities that are performed within an organization that are always ongoing despite a disruption. Model monitoring helps keep a business running round the clock by ensuring that the machine learning models in production are constantly being monitored. This is also going to help drive the business operations forward.
6. Model monitoring ensures financial stability in business
Every business is expected to make money, the business first generates revenue to settle its operational costs. Financial stability now ensures that businesses can pay overhead expenses and still generate income. Financial stability is important to keep the business up and running. Model monitoring can help achieve financial stability in business by providing real-time machine learning predictions and forecasts which can help the business maintain its capital and ensure it doesn’t suffer unexpected losses.
Monitoring ML models should not be overlooked, it is a very important aspect of the machine learning life cycle. Model monitoring helps ensure that machine learning models are accurate thereby continuously adding value to your business, it is therefore advisable that your business start using model monitoring in its operations.
Model Monitoring: What it is & why does it matter?
How do I get started using model monitoring for my business?
First, make sure that your business is implementing MLOps, then choose a suitable model monitoring tool.
Is model monitoring beneficial to my business?
Yes, it is because it helps to drive business operations forward, it also helps with business analysis and insights amongst many other benefits.
Daniel M. – maxburst[@]linkdoctor.email
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