So what is MLOps? This is a topic we covered in this episode. We discuss the different aspects of MLOps, for instance, data, business requirements, and also measuring the performance metrics. We discuss also data quality and feature engineering and its impact on the ML pipelines as well. We also do a short introduction on the different tools used in MLOps, such as Containers, Kubernetes, and Airflow. And let us throw in one more technical term...data versioning. Give us a listen to understand what that is!
Learning Resources:
1. What is MLOps (https://whatis.techtarget.com/definition/machine-learning-operations-MLOps)
2. Getting started with MLOps (https://ml-ops.org/)
3. MLOps Fundamentals with GCP (https://www.coursera.org/learn/mlops-fundamentals)
4. Difference between Data Scientist and MLOps Engineer (https://towardsdatascience.com/data-scientist-vs-machine-learning-ops-engineer-heres-the-difference-ad976936e651)
5. Learn Docker (https://www.youtube.com/watch?v=fqMOX6JJhGo)
6. Learn Kubernetes (https://kubernetes.io/docs/tutorials/kubernetes-basics/)
8. https://www.deeplearning.ai/program/machine-learning-engineering-for-production-mlops/

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