From the course: Full-Stack Deep Learning with Python
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Introducing MLOps - Python Tutorial
From the course: Full-Stack Deep Learning with Python
Introducing MLOps
- [Instructor] An important part of the full stack deep learning pipeline is MLOps or machine learning operations. This is a set of practices and tools that combines machine learning systems with DevOps practices to automate, streamline, and manage the end-to-end machine learning lifecycle. You don't really use MLOps in the first two stages of full stack development, in project planning and setup and in data collection and labeling. MLOps really comes into play in the last two steps where you train and debug your model, tune your model using hyper-parameter tuning, and then deploy, test, and maintain your model. MLOps serves to automate the machine learning workflow, as expressed here in this very simple diagram. You start with a model, you implement and debug that model, evaluate that model, tune hyper-parameters, improve the model or the data that you use, and when the model meets requirements, you deploy to production.…
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