Post by account_disabled on Dec 27, 2023 5:35:46 GMT
But what we do is a central team located around the world. We are a team of data scientists, machine learning engineers, and software developers working through a hub-and-spoke model across the company. So we want to minimize the distance between ourselves and the experts in the company - our data and domain experts - by working in cross-functional teams, product teams across the company. We also want to increase the speed of moving machine learning models from (proof of concept) to production. That’s why we have analytics partners across the company, and we also have a Machine Learning Operations product team focused on creating microservices throughout the machine learning model lifecycle.
We want to take all the data that we consume as a company, from our molecular identification, to our clinical trials, to our commercial execution and the manufacturing and shipping of our products, and take it from databases, flat files, cloud storage and transform it For something that ultimately works for Job Function Email List the company and ultimately supports patients’ lives. That’s why we exist: we want to turn this data into reality. As a team, we've been around for about a year and a half, and we've been working on projects company-wide.
For example we are working with R&D to use knowledge graphs to identify molecules for insulin resistance; we are deploying different marketing mix model links and sales lift recommendation models in different business regions; and last but not least, we Recently deployed a deep learning machine learning model that uses visual inspection on our inspection lines. This is very important because it is an optimization of the existing process. However, it gave us a lot of skills on how to build real-time machine learning models in a very regulated setting, which is a setting, meaning good manufacturing practice.
We want to take all the data that we consume as a company, from our molecular identification, to our clinical trials, to our commercial execution and the manufacturing and shipping of our products, and take it from databases, flat files, cloud storage and transform it For something that ultimately works for Job Function Email List the company and ultimately supports patients’ lives. That’s why we exist: we want to turn this data into reality. As a team, we've been around for about a year and a half, and we've been working on projects company-wide.
For example we are working with R&D to use knowledge graphs to identify molecules for insulin resistance; we are deploying different marketing mix model links and sales lift recommendation models in different business regions; and last but not least, we Recently deployed a deep learning machine learning model that uses visual inspection on our inspection lines. This is very important because it is an optimization of the existing process. However, it gave us a lot of skills on how to build real-time machine learning models in a very regulated setting, which is a setting, meaning good manufacturing practice.