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Keep shoppingMLOps: Real-World Machine Learning Projects for Professional
1,167 students
MLOps: Real-World Machine Learning Projects for Professional
Build end-to-end ML pipelines with MLFLow, DVC, Docker, Flask, GitHub Actions, Chrome Plugging , and AWS
Created byDS with Bappy
Last updated 5/2025
English
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
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Course content
7 sections • 20 lectures • 2h 53m total length
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 time
Why Take This Course?
Get hands-on experience with modern MLOps tools
Learn how to manage datasets, track models, and deploy to production
Understand real-world DevOps practices applied to Machine Learning
Build a portfolio of deployable, full-stack ML projects
Gain job-ready skills for roles in MLOps, Data Engineering, and ML Engineering
Throughout 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