What will you in MLOps Fundamentals – Learn MLOps Concepts with Azure demo Course
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Understand the core principles and lifecycle of MLOps (Machine Learning Operations).
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Learn to integrate CI/CD pipelines in machine learning projects.
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Explore model versioning, deployment strategies, and monitoring techniques.
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Gain hands-on skills in automation, orchestration, and collaboration across ML teams.
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Apply tools like Git, Docker, MLflow, Kubernetes, and more in real-world scenarios.
Program Overview
Module 1: Introduction to MLOps
30 minutes
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What is MLOps and why it’s critical in modern ML systems.
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Key challenges in deploying and managing ML models.
Module 2: ML Lifecycle & Pipeline Structure
45 minutes
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Understanding stages: development, training, validation, deployment, and monitoring.
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Building scalable and repeatable pipelines.
Module 3: Version Control with Git & DVC
60 minutes
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Tracking code and dataset versions for reproducibility.
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Using Git and DVC for collaborative ML development.
Module 4: MLflow for Experiment Tracking
60 minutes
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Logging experiments, models, and metrics with MLflow.
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Model registry, tracking server, and reproducible pipelines.
Module 5: Containerization with Docker
45 minutes
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Creating containerized environments for ML projects.
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Building portable and consistent deployment setups.
Module 6: CI/CD Pipelines for ML Projects
60 minutes
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Automating training, testing, and deployment steps.
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Tools like GitHub Actions and Jenkins in ML workflows.
Module 7: Orchestration with Airflow/Kubeflow
60 minutes
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Managing end-to-end workflows for model training and deployment.
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Scheduling, monitoring, and retry mechanisms.
Module 8: Model Serving & Monitoring
60 minutes
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Deployment strategies: batch, real-time, and A/B testing.
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Monitoring model performance, drift, and feedback loops.
Module 9: Real-World Project: End-to-End MLOps
75 minutes
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Implementing a complete MLOps project pipeline from data to deployment.
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Best practices and lessons learned.
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Job Outlook
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High Demand: MLOps is a top skill for AI infrastructure and DevOps careers.
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Career Advancement: Roles like ML Engineer, MLOps Engineer, and AI Platform Architect are booming.
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Salary Potential: $100K–$160K/year depending on location and experience.
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Freelance Opportunities: MLOps consulting, deployment automation, and AI infrastructure design.
Explore More Learning Paths
Build a strong foundation in MLOps and Azure Machine Learning with these carefully curated programs designed to guide you through practical projects and end-to-end ML workflows.
Related Courses
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Complete MLOps Bootcamp With 10+ End To End ML Projects Course – Gain hands-on experience with multiple ML projects, covering the full MLOps lifecycle from model development to deployment.
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Azure Machine Learning & MLOps: Beginner to Advance Course – Master Azure-based MLOps workflows, including model building, deployment, and monitoring in production environments.
Related Reading
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What Does a Data Engineer Do? – Learn how data engineering practices underpin effective MLOps workflows, model deployment, and system scalability.
