Full Stack Deep Learning with Python


Full Stack Deep Learning
Understanding and expertise in all components and stages of building and deploying deep learning systems - getting deep learning systems from prototype to production.

Steps in Full Stack Deep Learning

  1. Planning and project setup
  2. Data collection and labeling
  3. Model training and debugging
  4. Deploying, testing, and maintenance

Machine Learning Operations (MLOps) is a set of practices and tools to streamline and automate the end-to-end machine learning lifecycle. This is a part of the full stack deep learning.

Components and Concepts of MLOps

  1. Version Control
  2. Continuous Integration
  3. Continuous Delivery
  4. Model Packaging
  5. Monitoring and Logging
  6. Model Versioning


Components and Features of MLflow

Full stack deep learning

Project Planning and Setup

Data collection and labeling

Model Training and Debugging


Deploying, testing, and maintenance

Abandoned the course....


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Thoughts 🤔 by Soumendra Kumar Sahoo is licensed under CC BY 4.0