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 will demystify parts of the data science process used to create the all-important target variable and design machine learning experiments. We will 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.
In this episode
Founder & CEO , Sydney Data Science
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.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.