In our previous articles, we explored various components of UnifyAI designed to assist users in seamlessly taking their AI and ML use cases from experimentation to production. after successfully deploying models into production environments, one crucial aspect that gains paramount importance is the vigilant monitoring of the overall system. This monitoring process is essential for users to gauge the system’s health and ascertain whether the implemented system is functioning optimally or not.
Before UnifyAI, we gathered a lot of practical knowledge from dealing with real-life situations. We saw that sudden changes to different parts like data and models can easily lead to errors in the whole system. Figuring out which specific part of the system changed and caused it to be less effective often becomes complex. Before reaching an optimal solution let’s take a look at the what of monitoring in machine learning and artificial intelligence operations.
What is Monitoring in AI/ML systems?
In AI/ML operations, Monitoring is the process of observing, tracking, and evaluating the status, performance, or behaviour of a system, process, model or entity over time. Frequently, it is observed that different components in a system, where AI and ML models are deployed in production, generate trace data. When analyzed effectively, this data enables us to verify the system’s expected performance and even conduct predictive maintenance. Analysing this trace data and conducting maintenance or debugging on the system can be called Monitoring the AI/ML systems.
This highlights the critical role of monitoring in maintaining the reliability and performance of AI/ML systems in production. UnifyAI not only provides an advanced end-to-end system to easily and effectively take ML and AI models into production but also automates the monitoring of every single model deployed in the production using UnifyAI. Before understanding the UnifyAI monitoring system let’s understand more about the importance of applying monitoring systems to any AI/ML operations.
Importance of monitoring an AI/ML workflow
For a long time, machine learning and AI models were like secret boxes, not revealing how they make predictions. But when used through APIs, we can measure how well they perform in production using different matrices.
In real-world applications, understanding how well a model performs in production is paramount, even if we don’t delve into its inner workings. Hence, when monitoring an AI/ML system, it becomes crucial to focus on the following key areas:
Model Performance and Relevance: In real-world situations, it’s observed that the statistical patterns or distribution of the data used to train a model can change over time. This leads to challenges like data drift and model drift. This results in a decline in the performance of models in production and can even diminish their relevancy to the tasks they’re meant for. Thus, actively keeping an eye on these time-sensitive challenges is crucial to prevent models from losing accuracy or relevance in production.
Model Health and Availablity: There is no doubt in saying that AI/ML models are made accessible to users through APIs, which act as the interface for interaction. After this point, we can consider AI/ML models as an application which makes the prediction based on the given input. an abrupt surge in requests, whether anticipated due to increased user activity or unforeseen spikes in demand, can lead to system overload. This surge in demand can negatively impact the model’s health and availability of the model. By monitoring these situations, we can proactively predict downtime or debug and make decisions to uphold the well-being and accessibility of both the model and the system.
Model Usage and Scalability: This is an important aspect where monitoring holds immense importance in AI/ML workflows. Having the ability to track the resource utilization of a model in production allows for strategic actions such as scaling up when resources are abundant and scaling down when resources are constrained. This directly influences how efficiently the model and the resources are utilized and determines its scalability.
There are many such areas where AI/ML systems can be monitored and this monitoring can lead to various benefits. By looking at the above points we can say that a monitoring system can help in the Early Detection of Issues, maintaining model performance over time, optimizing resource utilisation and many more. The above areas are enough to establish monitoring as a crucial task to perform when AI and ML models are exposed in production.
Our AI platform UnifyAI offers a seamless approach to developing AI/ML models, encompassing data ingestion, experimentation with multiple models, and seamless deployment to production. Recognizing the complexities in maintaining of different building blocks of such a platform, we’ve integrated UnifyAI with a robust monitoring system. This system empowers users to oversee the entire ecosystem in one centralized location, enabling proactive decision-making and implementation to avoid potential failures. Let’s take a look and understand how this system of UnifyAI offers monitoring capabilities to future-proof AI systems in real-life scenarios.
UnifyAI Monitoring Toolkit
In the preceding sections, we’ve explored the what and why of monitoring in AI/ML workflows. It’s established that monitoring in AI/ML workflows is an essential undertaking. It serves as a crucial measure to mitigate the potential degradation of models and other system components as time progresses.
Since UnifyAI offers an end-to-end platform to serve models in production it is built with a monitoring system that uses multiple matrices, data and events in multiple stages and section of the UnifyAI and provides visualisation of multiple monitoring matrices. Let’s understand how this system works:
- Data Drift Calculation: Upon creating a model with unifyAI’s integration and development toolkit, the training data’s footprint is retained and statistically compared with the inference data produced by the model in the production environment. This process yields visualizations that offer insights into incoming data quality and features that change with time and can be subject to continuous monitoring. Additionally, as model inferences are factored in, it also provides indications of the model’s performance.
- Logging of APIs events: As discussed earlier, models are exposed in front of the world using APIs, Hence, it’s crucial to monitor APIs closely. The UnifyAI ecosystem is specifically structured to Log all critical observations and events in real time when APIs are actively utilized. These logs are presented in a clear and intuitive manner, allowing anyone to easily assess the real-time health of the APIs.
- Model containerization: UnifyAI employs containerization of models to enhance performance, focusing on speed and minimizing response errors. This approach not only streamlines the entire process but also facilitates scalability. It enables efficient measurement of resource utilization and represents it on the monitoring dashboard, whether in a static or real-time context.
- Monitoring dashboard: In the previous section, we identified the key metrics needed to monitor an AI/ML system. With the UnifyAI monitoring toolkit, this process is streamlined. The toolkit is purposefully engineered to automate these essential calculations. Additionally, it incorporates a user-friendly dashboard that provides real-time visualizations of these metrics in one place, whether it is visualisation of data drift, Logging of API events or Resource Utilization.
Through our extensive experience with real-life AI/ML projects, we identified the need to incorporate the mentioned methods into UnifyAI. This ensures that users have the capability to effectively monitor data, model, and system performance.
UnifyAI’s monitoring system serves as a pivotal component within the larger context of UnifyAI, transforming it into a future-proof AI platform for many AI/ML use cases. This comprehensive platform offers a seamless, effective, efficient, and scalable solution to guide AI and ML use cases from experimentation to production. Let’s understand what is UnifyAI?
What is UnifyAI?
DSW’s UnifyAI is an end-to-end MLOps platform that combines all the necessary components for seamless AI/ML implementation. Eliminating disjointed tools and manual processes is one of the key features of UnifyAI. By combining data engineering, feature engineering, MLOps, model monitoring, and many other processes, it provides a unified and cohesive environment for end-to-end AI/ML development, right from experimentation to production.
Automation is a core feature of UnifyAI, reducing the time, cost, and effort required to experiment, build, and deploy AI models. UnifyAI reduces the time and effort required to build and deploy AI models. There are various other factors about UnifyAI that enhance the scalability of AI/ML use cases and allow enterprises and organizations to scale their AI initiatives across the organization, from small-scale projects to large-scale deployments. UnifyAI provides the necessary infrastructure and computational power to handle diverse data sets and complex AI algorithms, ensuring that enterprises can effectively leverage the potential of AI at any scale.
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About Data Science Wizards
DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.
Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.
Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.
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