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Model Monitoring :

Key to moving from POC to production



Artificial intelligence is becoming increasingly vital in everyday life, impacting decision-making, education, and transportation, including driving cars. It's gradually replacing many conventional methods across various sectors. In industries like manufacturing, where traditional approaches such as lean manufacturing have long been used to optimize processes, manufacturing companies are now using computer vision (CV) based AI systems to gain visual insights into the work. An example of this is, how i-5O has replaced the andon light system with an improved CV based real-time alerts.

Regular maintenance becomes crucial to fully utilize the benefits of these computer vision systems. Without this upkeep, unforeseen issues can quickly transform a good model into a liability. This highlights the significance of why the model fails and how we can actively monitor CV-based systems to ensure their continued effectiveness and reliability.

Outdated computer vision models

Computer vision models can become outdated over time due to various factors encountered during their transition from the training phase to deployment. In the training phase, models operate in very controlled environments so they don’t face any major issues. However, when the models are deployed in production, they can encounter real-world conditions and challenges that may include changes in lighting, variations in object appearance, or unexpected obstacles in the environment. As a result, computer vision models may struggle to accurately make predictions on new data and make reliable predictions.
Therefore, it's essential to implement robust model evaluation solutions which could help in continuously evaluating the performance of these models in production. Model monitoring is such a solution that can help detect any changes in model performance, allowing for timely actions and updates to ensure the models remain effective and consistent throughout their life.

Model monitoring

In simple terms, model monitoring is like an alert system for checking the performance of CV models, helping to identify any performance issues at the moment. This practice not only simplifies the process of finding the actual root cause of a model’s lower accuracy results but can also assist in solving other issues impacting model performance. Overall, when you have a computer vision system, it’s not just “set it and forget it”. By continuously monitoring the results of the model, manufacturing companies can ensure that the CV system remains updated and aligned with evolving operational needs.

Challenges of model monitoring in production

While model monitoring is crucial for maintaining performance, it's not the only thing to consider when ensuring a model performs accurately and up to standards. Challenges like obtaining sufficient high-quality data, integrating ML models into existing systems, and overcoming organizational hurdles for ML adoption also require attention. Communicating the importance of data quantity and quality early on, considering integration needs in development, and addressing organizational barriers are essential for effective computer vision adoption and successful project outcomes.

Strategies for effective model monitoring in production

To tackle the challenges associated with model monitoring in production, it's important to implement effective strategies that ensure the model's performance is continuously improved. Rather than viewing model monitoring as merely a task for maintaining performance, it should be approached as a proactive solution to address potential issues before they escalate in a CV system. This needs the implementation of a robust data gathering process which can ensure a steady stream of good quality data , as well as thorough consideration of integration needs before integrating CV models into existing systems.
Organizations must actively work to overcome any internal barriers that may hinder the adoption of CV technologies, fostering a culture that prioritizes the effective use of these tools for achieving successful project outcomes.

Ensuring sustainable success for computer vision system

Implementing these model monitoring strategies effectively will ensure the sustainable success of CV models in production environments. In computer vision-based systems, it's essential to regularly assess the model's performance using real-time data to guarantee accuracy. Setting up triggers to promptly notify teams of significant deviations in key metrics can help identify potential issues such as model drift.
Integrating human oversight by visually inspecting labeled data at random intervals adds a layer of quality control. Root cause analysis should be conducted systematically to identify and address any underlying issues or changes within the system. Additionally, deploying new models in shadow mode initially allows for thorough performance comparison before full deployment, ensuring a seamless transition and minimizing disruptions. These proactive measures collectively contribute to maintaining the integrity and optimizing the performance of CV models, effectively addressing challenges encountered in real-world scenarios.

How I-5O performs model monitoring

using i-5O’s model monitoring system, manufacturing clients can optimize their processes in real-time, which means they can make adjustments immediately as needed. This system provides real-time alerts tailored to the client’s requirements, enabling prompt action whenever a process fault is detected. Through strategic planning and addressing key challenges upfront, i-5O ensures successful ML deployment, offering lasting value to customers and ensuring top-notch service quality.

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