Training Machine Learning Models in Pharma and Biotech Manufacturing with Aizon
By Rumi Olsen, Partner Solutions Architect at AWS
Creating and training machine learning models has become less time consuming and more cost efficient thanks to technology advancements like open source software libraries and Amazon EC2 P3 Instances, which accelerate machine learning and high-performance computing applications with powerful GPUs.
While market adoption of machine learning (ML) varies across industry segments, healthcare and life sciences have lots of opportunity to explore. Pharmaceutical and biotech industries, specifically, have low utilization of the large volumes of data they collect and store for regulatory purposes.
These solutions leverage the large volume of data to create artificial intelligence (AI) and ML capabilities to solve specific problems and support life sciences manufacturing.
Aizon allows manufacturers to discover new ways to optimize processes, reduce manufacturing quality issues, and enhance regulatory compliance. They incorporate predictions based on machine learning and advanced analytics.
One example of an AI implementation is called Predictive Overall Equipment Effectiveness (OEE), which increases the value of traditional OEE by finding hidden relationships between the equipment’s availability.
Visualizing Relationships in the Data
Aizon helps manufacturers develop their own custom machine learning predictive model by providing key features that walk them through the ML processes.
After uploading the data to a cloud storage, users can implement unsupervised AI algorithms that explore the data and find patterns hidden within. This capability greatly simplifies the process of selecting appropriate data sources for training predictive models. For example, if you want to predict oxygen concentration for a batch, the platform helps answer question such as, “What data should be used to train a model to make a prediction?”
If you have a large number of attributes, a question like this would not be easy to answer. Using the built-in unsupervised AI models, however, visualizes and shows you such relationships.
Figure 1 – Examples of Aizon visualizations of correlation of data attributes.
Choosing the Right Machine Learning Algorithm
Another Aizon feature is discovery, which helps manufacturers perform the initial data exploration that guides them to the appropriate ML algorithm they should use to train.
Machine learning practitioners spend the majority of their time exploring, preparing, integrating, and analyzing data for model training. The discovery feature helps you do that quickly and efficiently.
Figures 2 and 3 show how users can explore patterns within the data prior to training predictive AI models.
Figure 2 – Users can create an AI model using this graphical interface.
Figure 3 – Visual explorations of data relationships in Aizon.
The trained model will be used for capabilities such as Predictive OEE. Figure 4 shows a real-time visualization of a production line and predicted values—namely, Quantity to be produced and Quantity after 30 minutes.
Manufacturers can use those predictions to better understand how their equipment is performing and when to expect the production to be done. They could also use this information to plan when to service the equipment by looking at its overall performance.
In this way, manufacturers can proactively replace parts instead of letting them run until they stop. With ML models, the overall efficiency of manufacturing processes can be optimized.
Figure 4 – A real-time visualization of a production line with Predictive OEE.
Aizon helps manufacturers enhance the utilization and management of data and leverage the benefits of AI and ML capabilities.
Aizon is targeted toward the pharmaceutical industry, but the same technology or method can be applied to manufacturers in any industry. Contact Aizon if you’d like to get more information about the platform.
Aizon – AWS Partner Spotlight
Aizon is an AWS Partner that offers a number of machine learning solutions to enhance utilization and management of such data in a way that adheres to Good Manufacturing Practice (GMP) in the cloud.
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