Machine Learning Deployment Types - AI Summary

In this episode we cover: Machine Learning Deployment Types. Experiment/Model Tracking. Spark - Architecture. Kafka -Reading Data (Basics). MLOps Fundamentals or What Every Machine Learning Engineer Should Know. There are many ways you could deploy a machine learning model to serve production use cases. Even if you will not be working with them day to day, the following are the four ways you should know and understand as a machine learning engineer: 1. Batch: You apply your trained models as a part of ETL/ELT processes on a given schedule. You load the required features from a batch storage, apply inference, and save inference results to a batch storage. It is sometimes falsely thought that you can't use this method for real-time applications. Inference results can be loaded from a batch storage and used for real-time applications. 2. Embedded in a Stream Application: You apply your trained models as