MLOps Training (Class-2): ML Lifecycle & Git/GitHub for ML Projects | Hands-On Guide

MLOps Training (Class-2): ML Lifecycle & Git/GitHub for ML Projects | Hands-On Guide Welcome to MLOps Class 2 in our complete MLOps series! In this session, we dive deep into the machine learning lifecycle with a focus on: 🔹 Model Training & Hyperparameter Tuning 🔹 Model Evaluation with Accuracy, Precision, Recall, F1, R², MSE 🔹 Model Deployment using APIs, Docker & Kubernetes 🔹 Model Monitoring, Maintenance & Retraining (Feedback Loop) 🔹 Experiment Tracking & Version Control in MLOps 🔹 Git, GitHub & DVC for code, data, and model versioning 🔹 Hands-on GitHub repo creation, git add/commit/push workflow Timestamps: 0:00 - The Complete MLOps Lifecycle 9:39 - Why Version Control? Git vs. GitHub Explained 17:02 - Hands-On Git & GitHub Setup 21:23 - Live Demo: Making Changes & Pushing Code 36:24 - Real-World Collaboration & Course Wrap-up By the end of this class, you’ll understand how to: ✔ Train, test, and tune ML models for production ✔ Deploy models using industry-standard tools ✔ Monitor models in real-world environments ✔ Use Git & GitHub effectively in MLOps projects 📌 This is Part 2 of our MLOps Training Series. 👉 Watch Class 1 here: https://youtu.be/JFmvnBa3V3I 🔔 Don’t forget to subscribe for upcoming classes in the MLOps series, including CI/CD for ML, ML pipelines, monitoring with MLflow, and more! #MLOps #MachineLearning #GitHub #AI #DataScience #devops What we will cover: gen ai course artificial intelligence2025 genai roadmap generative ai full course generative ai explained genai projects machine learning genai gen ai full course generative ai course genai explained generative ai project gen ai tutorial python gen ai tutorial for beginners yt:cc=on genai prompt engineering gen ai tutorial for developers generative ai generative ai tutorial generative ai roadmap gen ai tutorial edureka machine learning algorithms