Free Download Udemy - MLOps Real-World Machine Learning Projects for Professional
Published: 4/2025
Created by: DS with Bappy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 20 Lectures ( 2h 54m ) | Size: 2.63 GB
Build end-to-end ML pipelines with MLFLow, DVC, Docker, Flask, GitHub Actions, Chrome Plugging , and AWS
What you'll learn
Build and deploy real-world machine learning models using MLOps Tools
Implement a complete Google Chrome Plugging
Implement a complete CI/CD pipeline for ML using GitHub Actions and model versioning
Track, manage, and compare ML experiments using DVC, MLflow for robust model governance
Design modular, reusable MLOps pipelines that follow industry best practices
Deploy and scale ML model on AWS cloud platforms with Docker production-ready architecture
Requirements
Basic knowledge of Python and machine learning concepts is recommended
Familiarity with Git and the command line will be helpful, but not mandatory
No prior experience with Docker, Kubernetes, or MLOps is required
Description
Welcome to the most hands-on and practical MLOps course designed for professionals looking to master real-world machine learning deployment.In this course, you won't just learn theory - you'll build and deploy production-grade ML pipelines using a modern stack including MLflow, DVC, Docker, Flask, GitHub Actions, and AWS. You'll even integrate ML models into a Chrome plugin, showcasing end-to-end MLOps in action.Projects You'll Build:- ML Sentiment Analyzer with MLflow & DVC- Reproducible training pipeline with DVC + Git- MLflow tracking dashboard with metrics & artifacts- Dockerized inference service with REST API- End-to-end CI/CD with GitHub Actions- Live deployment on AWS EC2- Chrome Extension that calls your ML API in real timeWhy Take This Course?Get hands-on experience with modern MLOps toolsLearn how to manage datasets, track models, and deploy to productionUnderstand real-world DevOps practices applied to Machine LearningBuild a portfolio of deployable, full-stack ML projectsGain job-ready skills for roles in MLOps, Data Engineering, and ML EngineeringThroughout this course, you'll work on production-grade ML projects that simulate real business use cases, incorporating tools and frameworks of MLOps. Whether you're looking to become an MLOps expert or deploy your first model professionally, this course equips you with the knowledge, code, and system design needed to succeed.
Who this course is for
Data Scientists and ML Engineers who want to deploy their models in production
AI Enthusiasts aiming to learn how ML systems work beyond model training
Anyone preparing for real-world ML interviews, startups, or enterprise-level ML deployment
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