As the demand for artificial intelligence (AI) continues to grow, organizations are seeking ways to effectively scale their AI initiatives. One crucial element that can significantly impact the scalability of AI solutions is the data and model management approach to how they are deployed. This article highlights the significance of integrating a streamlined framework for managing and deploying AI models, while also identifying the essential components required to do so.
Learn how a robust framework can orchestrate some of the most difficult aspects of an end-to-end AI lifecycle, including standardizing coding, tracking, and maintaining AI models.
Data Collection and Preparation
One of the most challenging aspects of building AI models is getting the necessary data in a usable format. For AI models to successfully make predictions that aid in better decision-making (or make decisions themselves), the data training a model must include dynamic scenarios that depict the complexity of a semiconductor manufacturing facility. These scenarios include fluctuations in work-in-progress (WIP) caused by downtime events, changes in certifications, bottlenecks, and more. Additionally, the data needs to be transformed into something meaningful, known as features. To do this, data scientists often start by utilizing custom code to extract data from various sources, followed by data validation to ensure its quality and completeness. This includes removing values outside acceptable tolerance and handling missing values according to their chosen methodology. Subsequently, they tailor the data transformation process to suit the specific requirements of their machine learning model.
There are also instances where data might be missing, such as when introducing a new part number or when a machine has been inactive, resulting in a data gap. In such cases, data scientists may resort to using their preferred simulation methods to generate the missing data and then do some ad-hoc feature calculations. While these custom AI deployment tasks serve their individual purposes, they lack scalability across the entire manufacturing facility or organization.
To provide the best opportunity for scaling, a standardized pre-configured feature management system is necessary to expedite data preparation. This key concept would enable any user in the fab or the organization who is attempting to develop other AI models to access a features repository. A sophisticated data management method should also go beyond simply automating feature management pipelines and allowing users to integrate their own intellectual property (IP). It should provide a variety of pre-built data validation checks, features that are ready to use, and simulation capabilities to help facilitate feature generation in the cases where data is missing. Additionally, non-coding options for standardizing, executing, and automating these tasks should be available when deploying models. By offering non-coding alternatives, such as a Web-based UI, a wider range of users can actively participate and contribute their expertise to the centralized feature management repository, which leads to more effective AI models.
Production Workflow
No one wants to work without a safety net. It is crucial to avoid situations such as an individual writing custom code on their personal machine and directly deploying their unique model to a production server without any transparency or accountability. The potential ramifications, such as inaccurate or maybe nonexistent predictions and unauthorized modifications to a production model, highlight the significance of implementing robust safeguards and maintaining accountability throughout the AI deployment process. Integrating AI into production should not be taken lightly. Therefore, a model management process that enables offline or preliminary scenario testing, model training, and model analysis is key. Necessary model validation methods could then be reviewed without impacting production.
The flexibility to choose between an on-premises (“on-prem”) or cloud option becomes essential when considering the preliminary stages of AI development. This is especially important when training a model on a large dataset, as it may require additional computational power. In an on-prem approach, tasks need to have the capability to be executed concurrently, taking advantage of parallel computing. Additionally, the versatility of a public, private, or hybrid cloud option also brings significant benefits. Providing this versatility when orchestrating AI elements ensures that end users have access to address their specific performance and data requirements effectively.
When models are deployed to production, it is essential for the system not only to automatically store previous versions in an archive location but also to provide comprehensive information about the production model. This information should then be easily available and include details such as training data, model parameters, prediction accuracy, and a clear record of the model’s deployment history, including the individual or group responsible for pushing it to production. It is also equally important to implement restrictions on user permissions. By allowing specific actions to be limited, such as restricting users to only view models without the ability to push them to production, a sense of control and accountability is maintained. This authentication, authorization, and model production workflow capability (covering preliminary stages, production, to archiving) fosters trust and, more importantly, efficiency in deploying AI by ensuring complete transparency for each model.
Monitoring and Maintenance
While there is a lot of buzz around AI and many successful use cases, people are still a little uncertain about it. This is largely because it can sometimes seem like AI operates like a ‘black box.’ Change management around AI requires users to trust the output, and a critical element in gaining that trust is explainability. A robust model management implementation should allow users (domain experts) to view analytics and reports that provide insights into the performance of a model and the reasons behind it. For instance, if a machine learning model is consistently showing a trend of high lot cycle time predictions, users should be equipped with the necessary tools and analytics to investigate the underlying causes. With such tools, they may discover that the high prediction is due to a WIP bubble at a specific step and that adjusting a particular dispatching rule could help address the issue.
Another challenge when deploying AI is maintaining it in a 24/7 environment. Eventually, there will be a shift in data, maybe due to the introduction of a new part or a machine deviating from its original functionality with age. By implementing automatic retraining methodology, models can be maintained at their optimal performance level regardless of data changes. A model management interface should enable users to set up automatic training triggers without the need for coding. These triggers can be based on time intervals, such as weekly updates, or condition-based retraining, such as when a feature begins to drift or the model’s accuracy starts to decline.
Another level of maintenance is enhancing a model once it has been deployed. Consider the scenario where an industrial engineer believes a quality feature could enhance the accuracy of the model. In this situation, the adoption of a user-friendly, web-based interface for the production model management workflow would empower non-data scientists such as this to effectively enhance that AI model. This interface promotes scalability by enabling data scientists to focus on addressing the next valuable AI business use case without getting bogged down by model maintenance or enhancements.
The last pain point for maintenance is having to manage and work with many different systems. By integrating your AI data and model management workflow around proven products already utilized in your factory, and with which there is familiarity, it becomes easier to scale AI. This then eliminates the need to adopt, integrate, and learn new software. By integrating with existing infrastructures, you not only streamline the process but also facilitate quicker and more efficient AI implementation.
Conclusion
Implementing a data and model management approach is vital for scaling AI initiatives effectively. A robust solution can orchestrate efficient management and deployment of AI models, empower engineers to interact fluently with AI, and overcome specific data and modeling challenges. This simplification and automation of the end-to-end AI operations enables organizations to achieve a significant competitive advantage.
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