Most MLOps discussion traditionally focuses on model deployment, containerization, model serving - but where do the inputs come from and where do the outputs get used? In this session we demystify parts of the data science process used to create the all-important target variable and design machine learning experiments.

We discuss some probability and statistical concepts which are useful for MLOps professionals. Knowledge of these concepts may assist practitioners working closely with data scientists or those who aspire to build complex experimentation frameworks.

Danny is a recovering data scientist who has moved over to the dark side of ML engineering in the past 2 years. He has spent multiple years deploying ML models and designing customer experiments in retail and banking sectors. Danny's passion is to guide businesses and individuals on their AI & machine learning journey. He believes a clear understanding of data strategy and applied machine learning will be a key differentiator in this brave new world. He currently provides personalised mentorship for 400+ aspiring data professionals through the #DataWithDanny community.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Danny on LinkedIn: https://www.linkedin.com/in/dannykcma/

MLOps #28 Continuous Evaluation & Model Experimentation // Danny Ma - Founder & CEO at Sydney Data Science 01:00:36 Share

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MLOps #28 Continuous Evaluation & Model Experimentation // Danny Ma - Founder & CEO at Sydney Data Science