Mark Mims

About Mark Mims

I\'m a Data Plumber. I spend most days designing/tuning/fixing/banging-on data pipelines and helping users manage the infrastructure behind data-intensive applications of all sorts. Originally trained as a Physicist (Ph.D. researching how quantum computers can learn to tolerate noise and errors), I\'ve since had an incredible career out in the world where Data Science meets DevOps and infrastructure engineering. I\'ve been lucky enough to work for companies like Canonical, the folks behind the Ubuntu operating system, Infochimps, DigitalOcean, and now Google. Places where talented teams of people are innovating and leading open-source communities and the tech industry as a whole. Current Passions- Scientific AI/ML. Using various deep or hybrid models to accelerate scientific high-performance computing (HPC) workloads - Operationalizing AI. MLOps and test-driven approaches to the data pipelines used to develop, train, and serve AI models in production environments Past obsessions include- training data scientists. I created and taught an online class that\'s essentially \"Just enough data engineering\" for data scientists in Berkeley\'s MIDS program - adopting test-driven methodologies into building data pipelines. keeping pipelines strongly connected to the actual business problems that need to be solved - and then pretty much anything to do with AI in infrastructure automation

Experience

1. Staff Solutions Architect - Applied Machine Learning, Google Cloud

   Google

   Apr 2019 — Present · Mountain View, US

   Helping users develop solutions on the Google Cloud Platform. Working with Google Cloud users and teams throughout Google:* SARS-Covid-2 Research Support - Representing Google as a rotating reviewer for the Covid-19 High Performance Computing Consortium (https://covid19-hpc-consortium.org/): “Bringing together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research” * Data Engineering / BigData - Helping users build data intensive applications on Google Cloud * Scientific AI/ML - Helping users accelerate scientific workloads on Google Cloud

Education

• The University of Texas at Austin

  Ph.D., Physics

  1992 — 2000

• The University of Texas at Austin

  B.S., Math

  1988 — 1992

• The University of Texas at Austin

  B.S., Physics

  1988 — 1992