Choosing The Right Methods
Specific AI methods have now established themselves as standards for some applications. For example, sophisticated tools are available for image analysis with convolutional neural networks (CNNs), image augmentation, and segmentation. However, it would help if you went into more detail. In that case, the concrete requirements for the individual use cases are rather specific, and tailor-made solutions and tuning options are required to achieve optimal performance and accuracy. New algorithms are published, and the existing ones are constantly being further developed. As a responsible data scientist, you enter a broad and dynamic field.
A comprehensive overview of methods and the ability to technically implement them for test and implementation purposes are required to make the right decision for each application. This also applies to considering whether conventional analytical methods do not already lead to the desired goal. As a result, a modern data scientist must position himself ‘technically’ and methodologically broadly. The complexity of the individual methods, in turn, requires specialization. Especially in the SME sector, the question arises regarding how much of this expertise is necessary in-house and how much external support makes sense to introduce the appropriate AI applications efficiently and quickly.
Development projects with universities, joint ventures, participation in working groups, or advice from specialized companies can have a supportive effect here. As a result, a modern data scientist must position himself ‘technically’ and methodologically broadly. The complexity of the individual methods, in turn, requires specialization. Especially in the SME sector, the question arises regarding how much of this expertise is necessary in-house and how much external support makes sense to introduce the appropriate AI applications efficiently and quickly.
Development projects with universities, joint ventures, participation in working groups, or advice from specialized companies can have a supportive effect here. As a result, a modern data scientist must position himself ‘technically’ and methodologically broadly. The complexity of the individual methods, in turn, requires specialization. Especially in the SME sector, the question arises regarding how much of this expertise is necessary in-house and how much external support makes sense to introduce the appropriate AI applications efficiently and quickly.
Development projects with universities, joint ventures, participation in working groups, or advice from specialized companies can have a supportive effect here. To be able to introduce the appropriate AI applications efficiently and quickly. Development projects with universities, joint ventures, participation in working groups, or advice from specialized companies can have a supportive effect here. To be able to introduce the appropriate AI applications efficiently and quickly. Development projects with universities, joint ventures, participation in working groups, or advice from specialized companies can have a supportive effect here.
Learning System
The prediction accuracy of AI models decreases over time. In contrast to conventional analytical methods, they can learn something new. The bases of evaluation can be adapted to changing conditions as required, either by self-learning (machine learning approach) or by a controlling expert. For example, previously unknown artifacts in microscope images or previously unknown sensor signals can be learned afterward due to new deviations in processing.
In addition to selecting, implementing, and providing suitable and optimally parameterized algorithms by a data scientist, easy access to the models by subject matter experts for tasks such as model training, result visualization, model versioning, Model release, and model monitoring are also mandatory. However, suppose the models used in production remain in the hands of the model developers, without the users monitoring them independently, ‘feeding’ them with new information, explaining the results, and being able to determine the specific use flexibly. In that case, the prediction results will become inaccurate over time and are therefore not accepted by the users in production.
IT Requirements
To reliably operate AI methods in the production environment, a consistent workflow is required for all types of applications occurring in the company, from the creation, parameterization, release, monitoring, and management of the models, the model versions, and their training and result data. Integrating these business processes into the IT landscape of industrial companies can be complex.
Three areas of expertise need to be combined: technical expertise for specific use cases, expertise in selecting and implementing the right AI methods, and expertise in IT integration and workflow design. The people involved have very different skills and working methods. While users and engineers are usually very reluctant to deal with programming or complex parameterization, data scientists deliver their results in (e.g., Python) scripts.
IT employees are then tasked with monitoring the trained models with MLOps methods, ensuring rollbacks to historical training states, backward compatibility of trained models after system component upgrades, and providing the business processes mentioned above. Several other requirements for data provision, real-time, deployment, maintenance, setup effort, traceability, robustness (24×7), and user management must be considered. However, integrating these IT requirements with a clear concept is a prerequisite for introducing specific use cases.
Isolated Solutions
A rather heterogeneous plant landscape is typical in the industrial environment, especially in the high-tech sectors. For example, inspection systems and microscopes do not necessarily come from just one manufacturer (best of breed). OEMs are also increasingly trying to integrate AI applications into their systems. This offers the advantage that all data the machines generate should be available for analysis comprehensively and without interface problems.
However, if these integrated systems are not flexible enough to adapt to existing AI and company-specific requirements (see above), their use is only possible to a limited extent. This danger exists particularly when the development of integrated AI systems is not the focus of the system manufacturer (add-on). Another disadvantage can be that machine-external data cannot be integrated into the tool-internal AI recipes, and the analysis options are limited to the corresponding system-related data.
If you are dealing with different OEMs, a uniform approach to introducing AI applications in production can pose an additional challenge or make it impossible. Therefore, an AI framework independent of system manufacturers and flexibly embedded in the IT landscape is recommended. Make this impossible. Therefore, an AI framework independent of system manufacturers and flexibly embedded in the IT landscape is recommended. Make this impossible. Therefore, an AI framework independent of system manufacturers and flexibly embedded in the IT landscape is recommended.
Also Read: Backend: What Is It, What Is It For, And How Is It Built?
