https://github.com/muthukamalan/mlops

Best practise and tips
https://github.com/muthukamalan/mlops

Last synced: 8 days ago
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Best practise and tips

• Host: GitHub
• URL: https://github.com/muthukamalan/mlops
• Owner: Muthukamalan
• Created: 2024-09-02T18:36:04.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-12-17T20:23:23.000Z (23 days ago)
• Last Synced: 2024-12-17T20:28:29.517Z (23 days ago)
• Language: Python
• Size: 5.88 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# MLOps Basics

- [X]	**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.

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

- [X]	**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.

- [X]	**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.

- [X]	**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.

- [X]	**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.

- [X]	**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.

- [X]	**AWS Crash Course**	A session on AWS, covering EC2, S3, ECS, ECR, and Fargate, with a focus on deploying machine learning models on AWS.

- [X]	**Model Deployment w/ FastAPI**	A hands-on session on deploying machine learning models using FastAPI, a modern, fast, web framework for building APIs.

- [X]	**Model Deployment for Demos**	Gradio, an open-source platform for creating and sharing demos of machine learning models, and a session on Model Tracing.

- [X] **Model Deployment on Serverless**	An overview of Serverless deployment of machine learning models, including an introduction to AWS Lambda

- [X] **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.

- [X] **Kubernetes - I**	This session provides an introduction to Kubernetes, a popular container orchestration platform, and its key concepts and components.

- [ ] **Kubernetes - II**	In this session, participants will learn how to monitor and configure Kubernetes clusters for machine learning workloads.

- [ ] **Kubernetes - III**	This session will cover introduction to EKS, Kubernetes Service on AWS, Deploying a FastAPI - PyTorch Kuberentes Service on EKS

- [ ] **Kubernetes - IV**	This session covers EBS Volumes, ISTIO and KServe, learning to deploy pytorch models on KServe

- [ ] **Canary Deployment & Monitoring**	This session covers how to deploy models with Canary Rollout Strategy while monitoring it on Prometheus and Grafana

- [ ] **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.