There are many ways that companies can monitor their models to ensure that they are performing well. This post will look at how model monitoring and automation will help you meet your business objectives.
What is Model Monitoring?
Model monitoring is the process of closely monitoring the performance of machine learning models in production so that production and AI teams can see possible problems before they hurt the company. Model Monitoring helps businesses:
- To determine if the model's data inputs are the same as during training.
- Ensure models are performing as expected
- Alert users on data or model drift so that action may be taken as soon as the model breaks some of the major underlying assumptions.
- Remove the model from the decision process for a vital business process instantly.
Why is Model Monitoring Important?
Model performance will deteriorate regardless of how well you've trained your models. Monitoring ML models in production is crucial for any ML project to prevent performance deterioration. Without it, you cannot identify which models are doing better. It is also the only way to find out when your model needs to be tweaked or if you should start over from scratch because the task is too complex.
There are several reasons to keep an eye on machine learning models. It helps to:
- evaluate prediction accuracy
- reduce prediction mistakes
- fine-tune models for optimal performance
Here are some more reasons why you should monitor machine learning models:
- It avoids poor generalization
Sometimes there isn't enough labeled data or other computational constraints. A model is usually trained on a subset of the total data in the domain. Monitoring models can solve it.
- Assuring the prediction's reliability
Machine learning models do not have independent inputs. As a result, modifications to any aspect of the system may result in surprising and unpredictable results. Model monitoring enables a very stable forecast by assessing many stability indicators.
- Identify flaws in your model
Monitoring discovers issues with your model and the systems that serve it in production before they start to produce negative business value. It also offers a way for maintaining and upgrading the model in production, guaranteeing that the model's prediction process is clear to important stakeholders for good governance.
Dealing with Model Degradation
Here are some strategies for detecting model degradation before it significantly impacts your business.
Checking model predictions
- Checking if the model's predictions are still valid or accurate is a standard way of identifying model deterioration.
- Compare ML models' predictions to real-world data.
- The score distribution generated by the model changes unexpectedly.
Changes in data distribution
- Most of the models saw significant changes during COVID-19. The data distribution has moved drastically as a result of these situations. The shift in data distribution indicates that the machine learning model needs to be changed.
- One of the most efficient approaches to detect model degradation is monitoring the input data provided to the model to see whether it has changed; this includes data drift and data pipeline problems.
- Defining metrics to keep track of the differences between data used to train the model and data submitted to the model for scoring. If the difference exceeds a certain threshold or is considerably drifting, it's a good indication of model drift and deterioration.
Concept drift evaluation
- Concept drift may be analyzed using the same approaches as data drift distribution analysis. In most circumstances, you'd compare your training set's label distribution to that of your production data in real-time.
- You can also evaluate if the input values are inside an acceptable set or range and if the frequencies of each value within the group correspond to what you've observed before.
Recommended Reading: What is concept drift and why does it go undetected?
Need to Automate Model Monitoring
Monitoring is one of the most challenging tasks since it is "important but not urgent." Automating your machine learning model monitoring system is a preferable approach. It provides you the assurance that things will operate as they should, emphasizing the importance of issues when they emerge.
Things to consider while automating model monitoring
The purpose of monitoring isn't to be notified after a failure has occurred. Your aim is to be informed far in advance of a model's performance degradation so that you may act proactively.
- All model components should be monitored, including operations, quality, risk, and procedures.
- Detecting metrics and outcomes that surpass thresholds and controls.
- Identifying phases and activities in the model operations process that are missing.
- Performing data integrity checks along the pipeline.
- Getting insight into the current state and status of all models.
Monitoring with Censius
Censius is a platform that allows you to monitor and identify issues automatically, as well as track the health of all your models in one place. With Censius, teams can design higher performing and responsible models by explaining model decisions, which will boost confidence among all stakeholders.
- In only a few lines of code, you can register the model, log features, and capture predictions.
- You can track the complete ML process by choosing from hundreds of monitor configurations.
- Keep track of issues and investigate problems without creating any code.
- Continuously monitor model inputs & outputs.
- Keep an eye out for data, prediction, and concept drift.
Many challenges can arise while monitoring production models, including feature drift, errors, outliers, debugging issues, etc. With Censius AI Observability Platform, ML engineers and data scientists can monitor different parameters like performance, traffic, data quality, drift, and more.
Your machine learning monitoring outputs will be shown on dashboards or sent out as part of an alerting system, which are the two major methods to convey model performance.
Censius user interface makes adding/updating models, projects, datasets, and much more a snap. You can even select the type of model monitors you want or the data feature on which you want to add the monitor. Each series of machine learning models has its unique report, which displays a collection of crucial data about the model's output.
Alerts are essential components of automated monitoring since they are responsible for incorporating the word "automated" into the framework. You may use different types of monitors to focus on various aspects of a model. Censius will regularly monitor these characteristics and notify you if any violations occur.
Sign up for a quick demo of the Censius AI Observability Platform to get started with model monitoring
Model monitoring that is automated gives you the peace of mind that everything will work as it should, emphasizing the importance of issues as they arise. An ideal automated machine learning AI observability platform like Censius will alert you much before a problem arises and gives you relevant context to take measures. I hope you found the article helpful.