Managing sensor performance has become a must-have for manufacturers. The advent and rapid adoption of IoT technology, enabling smart manufacturing systems, has created a two-fold scenario. Intelligent systems at once provide for near-real-time systems monitoring and create a new prediction problem. Manufacturers must now forecast not only supply-chain issues and inventory but also the state of equipment based on readily available sensor data.
The use of AI and machine learning (ML) provides an efficient means of solving this challenge but comes with its own development and implementation problems.
The first challenge of creating an AI/ML algorithm that leverages existing sensor data to predict equipment failures comes down to resource constraints. One thing most manufacturers have plenty of is sensor data. On the other hand, what is often lacking are data scientists with the skills and expertise needed to leverage all of that data and convert it to ML algorithms.
But a solution is at hand: AutoML 2.0 tech can provide a full-cycle automation alternative to manual AI/ML development, allowing manufacturers without data science teams to fully leverage sensor data.
With AutoML 2.0, firms can leverage the wealth of data at a manufacturer’s disposal, to create ML/AI algorithms in a matter of days—all with existing development resources, such as business intelligence and/or data professionals.
Finding the right AutoML solution can be a tricky proposition because the number of vendors and options is extensive and often confusing. The data science process—the backbone of AI and ML—has multiple steps. One of the most time-consuming steps is what’s known as feature engineering.
During feature engineering, data scientists and domain experts analyze available data and determine how to combine and leverage the information to create optimal features that can train the ML algorithms. When opting for an AutoML solution to help automate this process, manufacturers should focus on how much automation the product provides for feature engineering. The less code needed, the faster the development lifecycle.
But developing AI/ML algorithms only addresses part of the problem. A common strategy for deploying AI/ML algorithms developed with AutoML systems involves using API. With API-based delivery of ML algorithms, production systems use the AutoML system’s API to “call” the AutoML platform and leverage the developed ML algorithm against production data. This makes it easy to retrain the ML algorithm without impacting production systems—and introduces enough latency in the process to make it unacceptable when dealing with smart manufacturing operations that rely on sensor data.
A new approach is the deployment of final ML algorithms using a container approach. Using containerized models, manufacturers can deploy ML algorithms that provide millisecond response times while still providing the separation between production data and the AutoML platform. Container-based deployments achieve a near real-time deployment that can still be retrained and managed without disrupting production systems.
Manufacturers rely on sensors and sensor analysis to maintain an efficient and automated production environment. Adding ML capabilities to predict failures can provide big maintenance-cost savings but is often challenging to deploy. Advancements in AutoML tech like AutoML 2.0 and the onset of container-based model deployment can provide viable solutions to address these challenges.
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