Organizations place a higher priority on using machine learning (ML) to make critical business decisions. Thus, it’s essential to check whether the models are accurate and reliable. Machine learning operations (MLOps) include monitoring and alerting models that enable businesses to spot problems with model performance and take corrective action before significant damage occurs.
In this article, we’ll describe the value of model monitoring and alerting in MLOps, examine some of the most prevalent obstacles to their implementation, and finally present some of the best practices for creating an effective system.
The vital role of model monitoring and alerting in MLOps
Businesses now rely heavily on ML because it helps them make better, more data-driven decisions. Yet, as the demand for ML models grows, it becomes increasingly important to guarantee their efficiency and sustainability in operations. Deploying and maintaining ML models demands particular practices and tools, and here MLOps get in on the act. Thus, a sufficiently robust suite of model monitoring capabilities should be considered before implementing MLOps at the enterprise.
Organizations can benefit from model monitoring and alerting solutions that help them identify and fix performance issues, avoid wasteful missteps, and guarantee that their ML models produce reliable results. Without sufficient supervision, models can deviate from their intended behavior, leading to inaccurate predictions with disastrous consequences.
Moreover, organizations can much more easily comply with rules and regulations requiring an explainable and transparent model monitoring and alerting. By keeping tabs on the model’s performance, businesses can learn what influences the model’s choices and improve the model’s transparency and reliability.
However, organizations should create strong systems that measure important parameters, discover abnormalities, and issue alerts in real time to accomplish effective model monitoring and alerting. These systems also need to be flexible enough to adjust to new challenges.
Critical challenges of model monitoring and altering
Let’s examine some of the significant obstacles businesses can face as they integrate these essential components of MLOps.
Dealing with the high volume and velocity of data generated by machine learning models
Organizations have to be able to easily modify their monitoring and alerting systems to account for the ever-changing nature of ML models.
Adapting to changing business requirements, model architectures, and data characteristics
Changes in the market, new regulations, or a company’s new strategic focus could all lead to new needs for the company’s operations.
The efficiency of your ML model can also be impacted by changes to the model’s architecture. As models get more complex, organizations will need to update their monitoring and alerting systems accordingly. An organization may need to adjust its monitoring practices or alert thresholds, for instance, if its input data changes.
Also, companies may need to adjust their monitoring and alerting systems considering the fact that changes in data characteristics might affect the performance of ML models. For instance, if there are shifts in the data’s distribution or the appearance of new data sources, the quality of ML models may suffer.
Ensuring that model monitoring and alerting systems are integrated with other components of the MLOps pipeline
Data scientists spend a significant part of their time developing solutions that integrate with their existing frameworks. Data scientists only spend 35% of their time actually doing data science, while the remaining 65% is spent on engineering duties like tracking, monitoring, configuration, managing compute resources, setting up serving infrastructure, extracting features, and deploying models.
By combining these features, businesses can monitor model performance in real-time and respond immediately to any problems that arise. Organizations can achieve the following benefits from combining model monitoring and alerting systems with the rest of the MLOps pipeline:
- Maintain a close eye on the model’s progress through the MLOps pipeline, from data import to model deployment.
- Gather information from multiple steps of the MLOps pipeline to fully reveal the model’s status.
- Troubleshoot the MLOps workflow as a whole, from data preprocessing to model training and model deployment.
So, how to address these challenges?
Model monitoring and alerting: best practices
Let’s look at some examples of successful model monitoring and alerting system practices to handle these problems.
Leveraging distributed computing frameworks
Distributed computing frameworks like Apache Spark and Hadoop allow businesses to rapidly process massive volumes of data by spreading it over several machines.
In order to process massive amounts of data, distributed computing frameworks typically split the work into smaller tasks that can run simultaneously across multiple channels.
Build flexible and adaptable systems
As business needs, model structures, and data characteristics evolve, so must the monitoring and alerting systems that businesses implement. Organizations can adjust their monitoring and alerting systems as needed as long as they are designed to be easily customized and configured.
If you want your monitoring and alerting systems to work swiftly and effectively, it’s a good idea to automate their integration with the rest of your MLOps pipeline. Manual configuration, implementation, and management of monitoring and alerting systems introduce possibilities of human mistakes, which can be mitigated through automation.
Ansible and Chef are two examples of tools you can use to automatically configure monitoring and alerting infrastructure, allowing for more hands-off integration.
Businesses can also use Docker and Kubernetes, two containerization solutions for deploying, managing monitoring, and alerting infrastructure. With these instruments, companies can containerize their monitoring and alerting infrastructure, making it easier to deploy and operate in a wide variety of settings.
Organizations can save money, stay in compliance with regulations, and improve the accuracy and reliability of their insights with the help of model monitoring and alerting systems. What’s more, organizations can overcome most challenges by managing the large amount of data produced by machine learning models, adjusting to shifting business needs, model architectures, and data characteristics, and guaranteeing integration with other parts of the MLOps pipeline adopting the appropriate automation practices.
By doing so, businesses can keep their model monitoring and alerting systems in sync with their company’s needs and aims, allowing them to make better decisions and boosting the efficiency of their ML models.