Fueling innovative software
July 15-18, 2019
Portland, OR
Tania Allard

Tania Allard
Cloud Developer Advocate, Microsoft

Website

Tania Allard (she/her) is a cloud developer advocate at Microsoft and a research engineer with vast experience in academic research and industrial environments. Her main areas of expertise are within data-intensive applications, scientific computing, and machine learning; one of her main areas is the improvement of processes, reproducibility, and transparency in research, data science, and artificial intelligence. Over the last few years, she’s trained hundreds of people on scientific computing, reproducible workflows, and ML models testing, monitoring, and scaling and delivered talks on the topic worldwide. She’s passionate about mentoring, open source and its community, and she’s involved in a number of initiatives aimed to build more diverse and inclusive communities. She’s also a contributor, maintainer, and developer of a number of open source projects and the Founder of Pyladies NorthWest UK.

Sessions

1:30pm–2:00pm Tuesday, July 16, 2019
Practical DevOps for the busy data scientist: Alice’s adventures in DevOpsland
ML Ops Day
 Location: E145/146
Tania Allard (Microsoft)
Average rating: *****
(5.00, 1 rating)
You're a data scientist interested in improving your workflows, but you've only heard the term DevOps and you don't understand what it is or how to it apply to ML pipelines. Tania Allard guides you through the practical steps to understand and apply DevOps principles and practices to your workflows. Read more.
5:05pm–5:45pm Thursday, July 18, 2019
What's your machine learning score?
Incorporating Artificial Intelligence
 Location: C123-124
Secondary topics:  AI Enhanced
Tania Allard (Microsoft)
Average rating: *****
(5.00, 2 ratings)
ML in production is different than ML in an R&D environment. Tania Allard dives deep into a number of techniques to test your ML quality and decay in your R&D and production environments appropriately. You'll see examples of issues commonly encountered in the ML area and how to test and monitor your data, model development, and infrastructure. Read more.