In this session, we will talk about ASML’s journey of building portable data-driven use-cases that can be deployed close to customers’ EUV machines. The major use cases deal with predictive maintenance for ASML's newest EUV machines. These machines create a lot of revenue for ASML's customers, and downtime of these machines costs a lot of money. Being able to predict downtime of various parts within these machines allows customers to replace these parts during regular maintenance and increase overall uptime. ML has proven to be an effective technique to predict failures.

 

In order to run ML models effectively data scientists and machine learning engineers need access to state of the art infrastructure and tooling. We will focus on the journey of building ML infrastructure in a complex environment focussing on the requirements and walk through the process of selecting the right tools and technology for data professionals to be successful.

In this session, we will talk about ASML’s journey of building portable data-driven use-cases that can be deployed close to customers’ EUV machines. The major use cases deal with predictive maintenance for ASML's newest EUV machines. These machines create a lot of revenue for ASML's customers, and downtime of these machines costs a lot of money. Being able to predict downtime of various parts within these machines allows customers to replace these parts during regular maintenance and increase overall uptime. ML has proven to be an effective technique to predict failures.

 

In order to run ML models effectively data scientists and machine learning engineers need access to state of the art infrastructure and tooling. We will focus on the journey of building ML infrastructure in a complex environment focussing on the requirements and walk through the process of selecting the right tools and technology for data professionals to be successful.