What will you in Complete MLOps Bootcamp With 10+ End To End ML Projects Course
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Master the full MLOps lifecycle including CI/CD, orchestration, and deployment.
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Implement 10 end-to-end machine learning projects with production-ready pipelines.
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Use tools like MLflow, Kubeflow, Docker, Kubernetes, FastAPI, and GitHub Actions.
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Automate model training, testing, versioning, monitoring, and scaling.
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Understand cloud-native ML development and DevOps best practices for ML systems.
Program Overview
Module 1: Introduction to MLOps & Setup
30 minutes
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Overview of MLOps, CI/CD, and pipeline automation.
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Setting up Docker, Kubernetes, and Python environments.
Module 2: Version Control & Workflow Automation
45 minutes
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GitHub Actions for ML automation and testing.
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Code and data versioning with DVC and Git.
Module 3: Experiment Tracking with MLflow
60 minutes
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Logging parameters, metrics, and models using MLflow.
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Comparing model runs and storing artifacts.
Module 4: Model Building & Training Pipelines
60 minutes
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Modularizing code with pipeline structure.
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Building, training, and evaluating ML models.
Module 5: API Development with FastAPI
60 minutes
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Creating REST APIs for ML inference.
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Building interactive endpoints for prediction services.
Module 6: Dockerizing ML Applications
45 minutes
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Containerizing ML apps using Docker.
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Creating reproducible environments and builds.
Module 7: Orchestrating Workflows with Kubeflow & Airflow
75 minutes
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Building DAGs for ML workflows.
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Automating tasks like training, testing, and deployment.
Module 8: CI/CD Pipelines for ML
60 minutes
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Automating model testing, packaging, and deployment.
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Integrating GitHub Actions and Docker into CI/CD.
Module 9: Deployment to Cloud & Kubernetes
60 minutes
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Deploying ML models using Kubernetes and FastAPI.
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Scaling and updating models in production environments.
Module 10: Monitoring & Model Drift Detection
60 minutes
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Setting up monitoring dashboards and alerts.
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Detecting model drift and triggering retraining.
Get certificate
Job Outlook
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High Demand: MLOps is essential in scaling AI solutions across industries.
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Career Advancement: Opens roles in ML engineering, DevOps, and AI operations.
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Salary Potential: $120K–$180K+ for MLOps engineers and specialists.
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Freelance Opportunities: End-to-end ML system deployment and maintenance services.
Explore More Learning Paths
Advance your MLOps expertise and learn how to deploy machine learning models efficiently with these carefully curated programs designed for end-to-end project experience.
Related Courses
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MLOps Fundamentals: Learn MLOps Concepts with Azure Demo Course – Understand the core concepts of MLOps and gain hands-on experience with Azure for machine learning deployment.
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Azure Machine Learning & MLOps: Beginner to Advance Course – Master the complete MLOps lifecycle, from model development to deployment and monitoring, using Azure tools.
Related Reading
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What Does a Data Engineer Do? – Explore how data engineering practices support MLOps workflows, model deployment, and scalable machine learning systems.
Last verified: March 12, 2026
