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

Practical DevOps for the busy data scientist: Alice’s adventures in DevOpsland

Tania Allard (Microsoft)
1:30pm–2:00pm Tuesday, July 16, 2019
ML Ops Day
 Location: E145/146
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists

Level

Beginner

Description

Everyone uses the buzzword DevOps. Pretty much every company has hired or is about to hire a DevOps engineer or specialist. But it’s less clear how this translates to machine learning. What’s more, it isn’t clear how it relates to your models’ development, training, and deployment.

Tania Allard takes it upon herself to debunk some of the DevOps terms and myths. She uses machine learning examples, but terms will be explained for data scientists, machine learners, and anyone willing to understand from scratch what DevOps is and how to use it. As you follow Tania through the wonderland of DevOps, you’ll go down the rabbit hole to understand what DevOps is and get some jargon debunked. You’ll reach the pool of tears, where she shares why everything is failing and what you should be automating. You’ll get some advice from a caterpillar on getting started with DevOps; and from the mad tea party you’ll learn the tools of the trade. Tania explores who stole the tarts and helps you put it all together, and you reach Alice’s evidence, where Tania provides you with a summary and some resources for your DevOps journey.

Prerequisite knowledge

  • A basic knowledge of machine learning
  • Familiarity with Python (useful but not required)

What you'll learn

  • Understand of DevOps and how it can improve their machine learning workflows
  • Learn what you need to automate the delivery of your data products and will have examples to draw upon
Photo of Tania Allard

Tania Allard

Microsoft

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.