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It can be tricky to explain MLOps to colleagues and managers who are used to traditional software engineering and DevOps, let alone your gran. We have to answer the 'Isn't that just DevOps?' question clearly, otherwise the challenges of MLOps will continue to be underestimated (potentially by us as well as others). In this session, we dive into what is new about MLOps and why current mainstream DevOps alone does not solve the problems.

Ryan Dawson is an Engineer at Seldon and the author of the article 'Why is DevOps for Machine Learning so Different?' You can find that article at https://hackernoon.com/why-is-devops-for-machine-learning-so-different-384z32f1

||Show Notes||  

LF AI Foundation Interactive Landscape: https://landscape.lfai.foundation/
Seldon Docs: https://docs.seldon.io/en/latest/
An awesome list of references for MLOps: https://github.com/visenger/awesome-mlops
Awesome production machine learning: https://github.com/EthicalML/awesome-production-machine-learning

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Ryan on LinkedIn https://www.linkedin.com/in/ryan-dawson-501ab9123/