Udemy – MLOps Masters 2025-1

Udemy – MLOps Masters 2025-1 Downloadly IRSpace

Apr 18, 2025 - 18:15 Updated: Aug 17, 2025 - 18:18

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Udemy – MLOps Masters 2025-1

MLOps Masters Course. In today’s rapidly evolving world of AI, deploying machine learning models in production and maintaining them at scale requires a combination of advanced tools, streamlined workflows, and robust operational methodologies. This MLOps (Machine Learning Operations) course is your ultimate guide to mastering the art of seamlessly and efficiently integrating machine learning into real-world production systems. Designed for data scientists, machine learning engineers, and developers, this course walks you through the complete machine learning lifecycle, from model development to deployment and monitoring. You’ll learn how to bridge the gap between data science and DevOps and implement reliable, scalable, and efficient pipelines for continuous integration and delivery of ML models.

What you will learn

Strong understanding of MLOps concepts and their importance in bridging the gap between machine learning and production systems.
Proficiency in using tools such as Git, DVC, Docker, MLflow, and Grafana to efficiently manage and monitor ML pipelines.
Learn to set up and use Linux commands and environments for a simple MLOps workflow.
Explore CI/CD deployment for machine learning projects using tools like GitHub Actions, Jenkins, and CircleCI.
Develop expertise in containerizing ML applications with Docker and creating custom Docker images.
Build complete machine learning pipelines for data ingestion, validation, transformation, model training, and evaluation.
AWS SageMaker integration to train, deploy, and serve ML models in the cloud.
Working with BentoML to deploy and manage machine learning models at scale.
Learn how to set up monitoring dashboards with Grafana to track real-time application performance.
Implement DVC to version control data and pipelines, ensuring repeatability in ML projects.

This course is suitable for people who:

Machine Learning Engineers: Looking to improve their skills in deploying and managing ML models.
Data Scientists: Interested in learning how to move ML models from testing to production.
Software Engineers: Looking to transition into the MLOps space and gain hands-on experience with tools like Docker, CI/CD, and cloud platforms.
DevOps professionals: Want to integrate ML workflows into existing DevOps pipelines.
Artificial Intelligence enthusiasts: want to explore the practical side of AI and ML systems.
Cloud Engineers: Focus on using cloud platforms like AWS for machine learning workflows.
Students and newbies: With basic knowledge of ML and Python, looking to build a career in MLOps.
Professionals transitioning to AI/ML roles: Looking for a structured, hands-on approach to learning MLOps tools and frameworks.

MLOps Masters Course Specifications

Publisher: Udemy
Instructor: Boktiar Ahmed Bappy
Training level: Beginner to advanced
Training duration: 11 hours and 40 minutes
Number of lessons: 63

Course topics

MLOps Masters Course Prerequisites

Basic Python Programming Skills – Familiarity with Python syntax and scripting is essential.
Fundamental Knowledge of Machine Learning – Understanding basic ML concepts like training, evaluation, and algorithms.
Basic Understanding of Git – Experience with version control systems is helpful but not mandatory.
Command Line Basics – Comfort with navigating and executing commands in the terminal.
Access to a Computer – A system capable of running Docker and handling machine learning workloads.
AWS Free Tier Account – Required for hands-on cloud exercises and deployment practices.
Internet Connection – Reliable internet for cloud integration and software installations.
Eagerness to learn – A curious mindset and enthusiasm to explore MLOps tools and concepts.

Udemy – MLOps Masters 2025-1