Continuously monitoring model performance, ensuring reliability, and deeply understanding models' behavior in real-world scenarios are essential — and this is where AI Observability for MLOps and LLMOps comes into focus. Yet, enterprises embarking on their AI journey face a fundamental decision on build vs. buy: “Is it more advantageous to build an in-house AI observability platform or invest in a commercial solution?”
Companies starting out with only a few models may consider building an in-house monitoring tool as a cost-effective way to test out the value of their models. Teams often use open source model monitoring tools to develop their in-house systems, yet face challenges including the need for regular tooling updates and maintenance, and the lack of enterprise-level AI expertise and customer support essential for success. In addition, as the complexity and quantity of models increase, an in-house monitoring tool may no longer be sufficient. Whether a company's early in its AI journey or deeply involved in deploying advanced models, evaluating several critical factors is essential to determine if building or buying an AI observability platform is right for the company’s AI strategy, and achieve responsible AI. Let’s explore the key considerations in detail:
Addressing a Broad Spectrum of ML Use Cases
Each ML model may serve different business use cases ranging from loan approvals, to product recommendations, search engines, sales/demand forecasting, and beyond. Each of these use cases require different model tasks and frameworks, ranging from regression to classification to time series, and beyond. Each of these models and algorithms require their own performance ML monitoring metrics, such as recall, accuracy, precision, F1-score, mean absolute error (MSE), normalized discounted cumulative gain (NDCG), mean average precision (MAP), etc. As the need to monitor more and more model tasks grows, companies will need to build out and support an ever-growing number of monitoring metrics to properly ensure model performance.
Can a homegrown solution accommodate this diverse range of model tasks effectively? Ensuring comprehensive coverage for various tasks demands significant development effort and ongoing maintenance.
Support for Different Data Modalities
Depending on the business use case, ML models may require the handling of different data modalities, including text, images, and tabular data. Say, there’s a need to deploy a natural language processing (NLP) model for a service training platform or a computer vision (CV) model to streamline car insurance claims, you will need an AI observability platform that can support these varying input and output formats seamlessly. This entails additional complexities in structured and unstructured data handling and processing.
Monitoring unstructured data is very different from monitoring structured data. Traditional ML monitoring methods, such as Jensen-Shannon Divergence (JSD) or Population Stability Index (PSI), effective for tabular data, are not directly applicable to text and image data due to the complexity of high-dimensional vectors. A different approach is required to effectively monitor text and image data.
Adapting to an Ever-Evolving Demand for New Metrics
Beyond standard performance metrics, businesses often require customized metrics tailored to their specific objectives and domain requirements. In addition, standard performance metrics, such as accuracy or precision, gauge a model's effectiveness; they often fail to align directly with business KPIs or metrics that resonate with executives and stakeholders. Say, a model is built to approve or decline loan applications. The model would be monitored to make sure it classifies correctly. On the other hand, executives will want to see how much revenue they are making off of each loan approved or how much they are losing for each bad decision the model makes. A custom metric, that’s unique to your business, would need to be created to measure that.
Building a platform capable of accommodating both standard and bespoke metrics adds another layer of complexity and development overhead.
Resource Allocation
Every ML team should consider whether investing time and resources in building a monitoring tool — a task outside their core competency — is truly beneficial, despite their capability to do so.
Developing and maintaining an AI observability platform requires significant human and financial resources, yet teams often overlook the hidden costs of developing a homegrown tool. Not only are skilled resources needed, but the integration of supplementary tooling, which can escalate the total cost of ownership (TCO). Moreover, the accumulation of technical debt poses a future challenge that can become increasingly burdensome.
As more production models, which range in model and data types, need to be supported, the allocation of skilled expertise (not only in monitoring but also in explainable AI, AI compliance and regulations) and financial resources become critical considerations in building and maintaining the homegrown tool.
Could these resources be more efficiently allocated to developing and refining core ML models, rather than duplicating efforts on an AI observability infrastructure?
Integrating with Different ML Tools
ML models may be built using different frameworks and deployed on various serving layers. A homegrown solution must continuously adapt to integrate seamlessly with new cloud and end-to-end ML platforms, requiring ongoing development efforts and potential compatibility challenges.
Ensuring Scalability and Throughput
As the number of ML models in production grows, the AI observability platform needs to efficiently manage varying scales and throughput requirements. Initially monitoring (and explaining) gigabytes of data and later transitioning to petabytes, irrespective of the data type — be it tabular, text, or image — demands a scalable platform capable of accommodating the escalating volumes of data throughout the AI journey. Scaling a custom solution to these increasing demands, without compromising on performance and reliability, can be a significant challenge.
Staying Ahead of Emerging Trends
Homegrown AI observability platforms might struggle to keep up with new trends in ML. Did anyone see the wave of Generative AI (GenAI) taking shape in modern companies as quickly as it did? Most companies did not. It takes a lot of engineering effort to introduce the new scoring metrics required to accurately measure the health of GenAI and/or Large Language Model (LLM) applications. Vendors in this space are acutely aware of emerging trends in AI and build new features to accommodate those trends in their commercial AI observability platforms.
Moreover, companies need to consider how emerging trends and technologies are shaping AI regulations and compliance. Not only do ML teams need to keep up with new LLM metrics but also keep abreast with meeting AI regulations and compliance.
So should you build or buy an AI Observability platform?
While a “good enough” homegrown tool can get an ML team started with monitoring and may offer greater flexibility initially, it is not sustainable in the long run.The long-term costs and complexities can outweigh the benefits, especially as the organization scales its AI initiatives.
Alternatively, purchasing a proven AI observability platform from a reputable vendor offers several advantages. These platforms are specifically designed to address the challenges of monitoring, explaining model predictions, and managing ML models at scale. They often provide a comprehensive suite of out-of-the-box features and capabilities, including support for diverse model tasks, flexibility in metric definitions, compatibility with various frameworks, and scalability to meet growing demands.
Choosing a commercial solution allows organizations to benefit from the vendor’s AI expertise and dedicated support, enabling internal teams to focus on core business objectives and ML model innovation. Partnering with a vendor specialized in AI observability guarantees timely access to the newest advancements and updates and white-glove customer support, eliminating the need for ongoing internal development and maintenance. This approach not only streamlines operations but also supports the achievement of responsible AI.
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