MLOps: Real-World Machine Learning Projects for Professional
Published 4/2025
Duration: 2h 54m | .MP4 1280×720, 30 fps(r) | AAC, 44100 Hz, 2ch | 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 mosthands-on and practical MLOps coursedesigned for professionals looking to master real-world machine learning deployment.
In this course, you won’t just learn theory – you’ll build and deployproduction-grade ML pipelinesusing a modern stack includingMLflow,DVC,Docker,Flask,GitHub Actions, andAWS. You’ll even integrate ML models into aChrome plugin, showcasing end-to-end MLOps in action.
Projects You’ll Build:
Why Take This Course?
Gethands-on experience with modern MLOps tools
Learn how tomanage datasets, track models, and deploy to production
Understandreal-world DevOps practices applied to Machine Learning
Build aportfolio of deployable, full-stack ML projects
Gainjob-ready skillsfor roles in MLOps, Data Engineering, and ML Engineering
Throughout this course, you’ll work onproduction-grade ML projectsthat 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
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