You evaluated the benefits of ML to your business and realized that it would boost your business considerably. You even went ahead to build and deploy your initial models in production. Congratulations!! You are an ML-ready enterprise now.
But that’s not the end of the road. Your responsibility increases now.
Production models must be monitored for their consistent performance and positive business value. So how do you approach this challenge?
When you dig a little about these challenges, you find that there are two options: either you build a solution in-house or you get a vendor who has a solution for you. We have already covered the “build vs buy” dilemma for model monitoring. In this article, we will focus on the buy part.
When it comes to buying a model monitoring solution, there are two options:
- Use open-source AI model monitoring tools
- Use proprietary or commercial AI model monitoring tools
Which is the better option to go with? Let’s find out through this article.
Things to Consider while Selecting a Model Monitoring Tool
Purpose of monitoring
First things first. When choosing the best tool for AI model performance monitoring, one should have a clear idea of what to monitor and how it will help. Broadly AI monitoring system is classified into these three categories.
Operational system monitoring
The constant monitoring of ML deployment infrastructure constitutes a significant step in handling deployment-related issues. Various metrics used for monitoring ML system operations include serving latency, throughput, systems’ uptime, disk utilization, CPU/GPU usage, and the number of API calls.
Input data monitoring
Consistent monitoring of model input data makes sense. Machine learning model degradation is expected if data quality issues such as data drift, skew, and concept drift are overlooked. Different input data monitoring metrics include:
- Data quality: Checks that the quality of data is consistent with the quality of training data
- Data consistency: Ensures that data used in production is consistent within a defined range. It also includes checking data type's consistency, data range, and any format errors
- Data drift: Tracks the changes in the distribution of data and the statistical structure of data
- Training-serving skew: It indicates a gap in the model’s performance during training and serving due to data changes, handling discrepancies, and feedback loop between model and algorithm
- Outliers: Highly different data points than the rest of the dataset. Many ML algorithms are sensitive to outliers and show poor performance
Model’s performance monitoring
Model staleness refers to the condition where it fails to perform as expected and needs to be re-trained. Monitoring model performance for specific metrics helps ensure that model is performant in production. Different performance metrics tracked are accuracy, sensitivity, specificity, recall, precision, F1 score, and more. Other model performance aspects include feature importance change, model fairness, numerical stability, and output distribution.
Recommended reading: Model Metrics
While selecting your AI model monitoring tool, you should consider a basic but important question – whether my monitoring tool serves all platforms and cloud infrastructures or it serves a specific infrastructure.
If your data science project scaling involves a single cloud environment, then it is preferred to go for the relevant ML monitoring tool supported by your cloud platform. The integration will be easy.
For a multi-cloud environment, choosing a platform-agnostic ML monitoring tool is wise. Such extensible ML monitoring solutions easily integrate with your current organization ecosystem.
Tool maturity level
When it comes to choosing a model monitoring tool, the reliability of the tool in production environments is an irreplaceable requirement. It is advisable to go for tools that have undergone a thorough maturity assessment. Well-tested and widely spread utilization of ML monitoring tools assures a better maturity level. Modern model monitoring platforms also empower users with sophisticated dashboards, powerful visualization, and analytics to make things easy.
Level of product support
For building a reliable and maintainable ML stack, it is imperative to have strong support for each ML stack component. The degree of product support turns out to be a key criterion.
A typical tradeoff involves additional costs incurred with commercial machine learning monitoring toolsets. Product support is well understood with different parameters such as the number of employees and contributors, community support, product documentation, and GitHub stars.
Another key concern is a mismatch between the levels of expertise that the product is designed for and that of the actual users.
Ease to use
Model monitoring tool should be easy to use and configurable by individual end-users instead of relying on the central ops team. The monitoring tool should integrate seamlessly with production data through database connectors, file transfers, and APIs.
Support for powerful visualizations, prepopulated dashboards, custom queries, and a management console adds to the user’s ease. Customizable ML monitoring platform allows users to
- Set custom dashboards
- Define custom metrics
- Specify test cases and checks
- Set custom thresholds
- Configure custom integrations and workflow
In the model monitoring context, production-grade scalability is a primary requirement. When it comes to operationalizing models, things become challenging. Platform scalability matters to keep up with the growing need to monitor thousands of production models and millions of predictions generated per second.
AI model performance monitoring tool scalability considerations are processing power, service latency, and storage amount.
Pros and Cons of Open-Source Model Monitoring Tools
Pros of open-source model monitoring tools
Cost-effectiveness is the first and foremost factor that comes with open-source ML model monitoring tools. Most proprietary model monitoring platforms are costlier than free open-source libraries.
Open-source AI model performance monitoring tools empower users with complete control and ownership. Data science teams can install and execute open-source libraries in their existing ML infrastructure.
No data privacy concern
An open-source model monitoring tool offers complete ownership of your data leading to no data privacy issues. These tools prevent data privacy issues by
- Supporting better control of your data.
- Ensuring compliance with your data privacy policies by tweaking tool settings and configurations.
- Auditing your open-source libraries to check data manipulations and make necessary changes.
Open-source community support
Open-source model monitoring tools are often supported by a community of users. Anyone can contribute new ideas, features, and enhancements to these free tools. Also, you get easy help to troubleshoot any issues.
As the codes of these libraries are readily available, anyone can access these and collaborate on them. With commercial machine learning monitoring tools, you have to rely on a specific group of experts to resolve issues. This can take more time to troubleshoot.
Cons of open-source model monitoring tools
As open-source tool’s code is readily available and accessible, anyone can update the code. This poses high-security threats to your ML infrastructure as the possibility of downloading malicious code is higher.
Adversarial attacks are not new to ML systems. Hackers and spammers manipulate ML models for purposeful outcomes. Using open-source libraries for model monitoring makes their job easy.
Lack of reliable support
For AI model performance monitoring, you prefer having dedicated support to resolve your issues. With open-source model monitoring tools, it is not always guaranteed. Proprietary model monitoring tools have SLAs to help you tackle any challenge that comes up in their tool usage.
Not a sustainable choice
An open-source ML monitoring tool would be a better choice from a cost perspective, but it won’t be a sustainable choice in the longer run. When the open-source community decides to close their shop, your responsibilities increase with maintaining and upgrading required monitoring features.
Pros and Cons of Proprietary Model Monitoring Tools
Pros of proprietary model monitoring tools
Faster adoption of monitoring tool
Proprietary monitoring tools allow seamless and faster adoption of ML monitoring systems in your ML infrastructure. Selection of the right commercial monitoring platform might be tricky and time-consuming. However, once you finalize a tool, the next steps are frictionless compared to working with open-source ML model monitoring tools.
Effortless model monitoring
Commercial AI model monitoring platforms make monitoring hassle-free. Commercial platforms can configure any number of monitors without specialized engineering efforts. You might question the lack of control over these proprietary tools, but the highly competitive ML tools market encourages vendors to consider this factor and improve.
Long-term and sustainable partnership
Once you choose your ML monitoring partner, it is not just a service-level agreement that you sign, but you build a strategic and sustainable partnership for the long run. This helps you follow standard industry practices, get deeper insights, and strategize.
Cons of commercial model monitoring tools
Selecting the right vendor
The biggest challenge in implementing a commercial AI model monitoring platform is the selection of a vendor and purchase stage. The technical team may lack the expertise required in procurement. Hence decision-making process often gets delayed.
Selecting the correct option that fits your model monitoring requirements is critical. But as the model monitoring market is relatively new, vendors delight prospects with initial commitment-free POCs, free trials for a few weeks, and platform demos to ensure a better choice.
Getting commercial model monitoring tool is advisable if you are looking for a long-term model monitoring setup. Your association with the platform vendor can help you overcome future challenges such as tweaking tools for specific features and AI monitors.
However, vendor lock-in can be the downside of some proprietary monitoring tools as it becomes difficult to switch between different vendors and ensure the same rapport. Cutthroat competition in this space encourages vendors to simplify things with plug-and-play options for machine learning model performance monitoring.
Costlier than open-source tools
Commercial AI model monitoring platforms cost higher than their open-source alternatives. However, this stretched budget gets compensated with reliable and complete support by the vendor and strategic association in your entire journey.
To summarize the above points:
Commercial or Open-source Tools: Which one’s for me?
Choosing between proprietary and OSS tools becomes easy with a clear idea of the budgets, skills, and timelines.
Selecting open-source monitoring tools is advisable if you are looking for a model monitoring option that offers flexibility and allows you to play around with the code to devise a customized solution. Also, be sure that you can spend the required time to set up the tool based on your needs.
On the other hand, go for proprietary tools if you are looking for dedicated support, the ability to plug and play, faster adoption, and integrations with other tools/platforms. Most proprietary tools these days also offer the ability to customize while ensuring your data and code are secured, which is not guaranteed with OSS options.
We prefer proprietary tools for four reasons:
- They are easy to set up
- They offer a plethora of features
- They save a lot of time in maintaining the monitoring solution
- They provide integrations with other tools used in the ML ecosystem
Taking this a step further, we would also advise you to consider our model monitoring solution - Censius. It is an AI Observability Platform that helps proactively monitor the entire ML pipeline for various issues. It allows you to readily scale your ML infrastructure with support to add-ons required with little engineering effort.
The Censius AI Observability Platform helps address challenges that might degrade your model’s performance and help every stakeholder associated. The platform provides an easy-to-understand user experience so all stakeholders can use the platform without any hassle.
Moreover, the platform is designed and built with continuous feedback from the ML community to ensure that it adequately addresses the unique needs of businesses. Some of the distinctive features of the Censius AI Observability Platform include:
- Cohort monitoring
- Setting up custom model monitors
- Customizing alert severity
- Plug-and-play integrations
- Explainability supported for model predictions
- Powerful visualizations
Curious to know more about the Censius AI Observability Platform? Sign up for a customized demo and resolve all your queries with our team’s assistance.
P.S. We also offer a 14-day, no-commitment free trial for users who want to experience the product. You can sign up for your free trial here.