Within the digital world of A.I. and ML, it is easy to get lost in the science of data modelling, rather than using an engineer’s eye to connect and automate systems. When automating with A.I. the magic can only happen when you have integrated the interface of these systems with one another. However every lab is different, and each will present a new set of challenges to automation. The only approach to automation is to balance the digital modelling with engineering direction.

Join this session to understand:

• Creating simple ML and A.I. foundations for troubleshooting clarity
• Piloting new A.I. and ML methods on currently automated labs.
•  Standardizing data collection from differing systems to provide clean and clear data and metadata
• Closing the gap between a labs automative potential and how its systems are capable of fitting together