Many scientific R&D organizations are interested in applying machine learning (ML) and advanced analytics (generally “AI/ML”) but are intimidated by lack of clarity on how to get started, the apparent complexity, or how to best achieve practical outcomes. With the ever-increasing speed, volume and complexity of data produced in laboratories it is essential to carefully consider how to best provide AI/ML efforts with required data.
In this webinar, we will discuss the basis of effective AI/ML approaches in R&D laboratories, including practices to ease the deployment and integration efforts for incorporating these advanced tools within your organization’s workflow. Topics covered in this webinar will include:
• Background of AI/ML in R&D laboratories
• Laboratory data flow and data architecture requirements: establishing digital continuity across laboratory process and the lifecycle of data
• Model building and application, with examples
• Practices for integrating and deploying AI/ML tools and building a Data Science capability
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