🔄 Bioprocessing specialists and data science experts need to communicate and find ways to integrate AI into workflows.
⚙ Data preparation, standard formatting, and clear definitions of data usage are crucial for success.
🧪 Lumilytics helps with end-to-end process optimization and smaller-scale projects such as automating data analysis.
📊 Understanding the capabilities and use cases of AI technology is essential for successful implementation.
Introduction:
The integration of artificial intelligence (AI) into bioprocessing requires a cultural change within companies, according to Varsha Daswani, PhD, a senior director at data science consultancy Lumilytics. Understanding the potential of AI and data science in biomanufacturing is crucial for maximizing its benefits, with the success often relying on thorough data preparation.
- Data science needs to be integrated into bioprocessing workflows, requiring communication and collaboration between bioprocessing specialists and data science experts.
- There is a wide variation in clients’ knowledge about AI’s capabilities in bioprocessing, ranging from implementing process modeling to using AI for running bioreactors.
- Data should be standardized and clearly defined in terms of how it will be used to ensure the success of AI projects.
- Establishing a use case is important for identifying bottlenecks in bioprocesses and determining the appropriate technologies and tools.
- Lumilytics offers customized solutions for biomanufacturing optimization, including end-to-end process optimization and automation of data analysis.
Conclusion:
Integrating AI into bioprocessing requires a shift in company culture and effective communication between bioprocessing specialists and data science experts. Standardizing data and clearly defining its use are critical to the success of AI projects in biomanufacturing. Lumilytics provides customized solutions for optimizing bioprocessing, offering both larger-scale process optimization and smaller-scale automation projects.
