MLOps: RealWorld Machine Learning Projects for Professional

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
Genre: eLearning | Language: English

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:

– 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?

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
– Anyone preparing for real-world ML interviews, startups, or enterprise-level ML deployment
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