EMLO4

Welcome to EMLO4

Pre-Requisites

• One of the main pre-requisite of this course is you should know how to work with deep learning networks in PyTorch (training and inferencing), as our world will revolve around that.
• Also, it's good to have experience working with Linux systems, but if not you'll definitely learn a lot on the way.
• It is assumed you are comfortable with Git and GitHub.
  
• You must finish the capstone project to be eligible for the course certification.
  
• You need to maintain a 70% aggregate score to be eligible to take part in the CAPSTONE project
  

Here is the complete Syllabus:

Pre-Requisites

• One of the main pre-requisite of this course is you should know how to work with deep learning networks in PyTorch (training and inferencing), as our world will revolve around that.
• Also, it's good to have experience working with Linux systems, but if not you'll definitely learn a lot on the way.
• It is assumed you are comfortable with Git and GitHub.
• You must finish the capstone project to be eligible for the course certification.
• You need to maintain a 70% aggregate score to be eligible for taking part in the CAPSTONE project

Session Name	Description
Session 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.
Session 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.
Session 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.
Session 4	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.
Session 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.
Session 6	Data Version Control (DVC) Data Version Control (DVC), a tool for managing machine learning data and models, including versioning, data and model management, and collaboration features.
Session 7	Experiment Tracking & 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.
Session 8	AWS Crash Course A session on AWS, covering EC2, S3, ECS, ECR, and Fargate, with a focus on deploying machine learning models on AWS.
Session 9	Model Deployment w/ FastAPI A hands-on session on deploying machine learning models using FastAPI, a modern, fast, web framework for building APIs.
Session 10	Model Deployment for Demos Gradio, an open-source platform for creating and sharing demos of machine learning models, and a session on Model Tracing.
Session 11	Model Deployment on Serverless An overview of Serverless deployment of machine learning models, including an introduction to AWS Lambda
Session 12	Model Deployment w/ TorchServe An introduction to TorchServe, a PyTorch model serving library, and a hands-on session on deploying machine learning models using TorchServe.
Session 13	Kubernetes - I This session provides an introduction to Kubernetes, a popular container orchestration platform, and its key concepts and components.
Session 14	Kubernetes - II In this session, participants will learn how to monitor and configure Kubernetes clusters for machine learning workloads.
Session 15	Kubernetes - III This session will cover introduction to EKS, Kubernetes Service on AWS, Deploying a FastAPI - PyTorch Kuberentes Service on EKS
Session 16	Kubernetes - IV This session covers EBS Volumes, ISTIO and KServe, learning to deploy pytorch models on KServe
Session 17	Canary Deployment & Monitoring This session covers how to deploy models with Canary Rollout Strategy while monitoring it on Prometheus and Grafana
Session 18	Capstone This session is a final project where participants will apply the knowledge gained throughout the course to develop and deploy an end-to-end MLOps pipeline.