Sash and his team began looking for additional ways to continue to advance the technology, including ways to advance how the team was looking at cases where outcomes don’t match predictions. He had already heard of Seldon and began the evaluation process just as Adrian Rossall joined the team. Adrian had also used Seldon to great success before and made it a priority to start the implementation quickly.

Sash chose Seldon Enterprise Platform, to assist with monitoring and managing models in production in order to enable them to scale. Seldon would speed up the debugging process to uphold the stability and reliability of those many models now in production.

“I liked that it was modular and we now have the observability in place and were able to simply port all our models across. It was painless to get started.”

Exscientia had to make a choice whether to change their model code or not when moving to a new MLOps platform. This was a pivotal decision as changing the code could have a detrimental slow down effect on the entire project. They decided to keep the same code and port over all their existing models. One benefit of implementing Seldon was that the code didn’t matter to Seldon and they were able to fit the architecture to the model code. That theory paid off and saved a lot of time.