TSAI-EMLO-4.0

🔬 EMLOV4 dives deep into the world of MLOps, exploring advanced techniques and tools crucial for success in production environments. From Docker and PyTorch Lightning to AWS and Kubernetes, this course equips you with the knowledge and skills needed to excel in the rapidly evolving field of machine learning operations.

Contains solutions for assignments and learning from Extensive Machine Learning Operations - Version 4.0 course of The School of AI https://theschoolof.ai/#programs

Website: [On Development]

1. Introduction to MLOps

   An overview of MLOps (Machine Learning Operations), covering the best practices and tools to manage, deploy, and maintain machine learning models in production.

2. Docker - I

   A hands-on session on creating Docker containers from scratch and an introduction to Docker, the containerization platform, and its core concepts.

   Learnt about docker fundamentals and how to chose the base docker image and reduce the size as minimal as possible

3. Docker - II

   An introduction to Docker Compose, a tool for defining and running multi-container Docker applications, with a focus on deploying machine learning applications.

   Learnt about docker compose and mounting multiple volumes and handling multiple containers

4. PyTorch Lightning - I

   An overview of PyTorch Lightning, a PyTorch wrapper for high-performance training and deployment of deep learning models, and a project setup session using PyTorch Lightning.

   Learnt about using lighting to train, eval and infer images using a model developed.

5. PyTorch Lightning - II

   Learn to build sophisticated ML projects effortlessly using PyTorch Lightning and Hydra, combining streamlined development with advanced functionality for seamless model creation and deployment.

6. Data Version Control

   Data Version Control (DVC), a tool for managing machine learning data and models, including versioning, data and model management, and collaboration features.

   Medium Blogs

   • Setting Up a Workflow with DVC, Google Drive and GitHub Actions

   • Setting Up a Workflow with DVC, Google Cloud Storage(GCS) bucket and GitHub Actions

7. Experiment Tracking and Hyperparameter Optimization

   A session covering various experiment tracking tools such as Tensorboard, MLFlow and an overview of Hyperparameter Optimization techniques using Optuna and Bayesian Optimization.

Important tools, method, configs, links

• resource file

Updates

• Every month end during course development

• There is a resource file in which i maintain that has concepts and tools which i learnt newly in EMLO-4.0 course.