MLOps and DevOps have a large number of parallels. Many of the techniques, practices, and processes used for traditional software projects can be followed almost exactly in ML projects. However, the day-to-day of an ML project is usually significantly different from a traditional software project. So while the ideas and principles can still apply, it’s important to be aware of the core aims of DevOps when applying them.

Damian is a Cloud Advocate specializing in DevOps and MLOps. After spending a year in Toronto, Canada, he returned to Australia - the land of the dangerous creatures and beautiful beaches - in 2018. Formerly a dev at Octopus Deploy and a Microsoft MVP, he has a background in software development and consulting in a broad range of industries. In Australia, he co-organised the Brisbane .Net User Group, and launched the now annual DDD Brisbane conference. He regularly speaks at conferences, User Groups, and other events around the world. Most of the time you'll find him talking to software engineers, IT pros and managers to help them get the most out of their DevOps strategies.

|| Links Referenced in the Show ||
MLOps, or DevOps for Machine Learning: https://damianbrady.com.au/2019/10/28/mlops-or-devops-for-machine-learning/
Microsoft Azure Machine Learning: http://ml.azure.com/
MLOps Coffee Sessions #6 Continuous Integration for ML // Featuring Elle O'Brien: https://www.youtube.com/watch?v=L98VxJDHXMM
MLOps: Isn’t that just DevOps? Ryan Dawson speaks at MLOps Coffee Session: https://www.seldon.io/mlops-isnt-that-just-devops-ryan-dawson-speaks-at-mlops-coffee-session/
DVC - Data Version Control: https://dvc.org/
Pachyderm - Version-controlled data science: https://www.pachyderm.com/
Databricks - Unified Data Analytics: https://databricks.com/

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