Udemy – Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins 2025-3

Udemy – Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins 2025-3 Downloadly IRSpace

Apr 18, 2025 - 19:10 Updated: Aug 17, 2025 - 18:51

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Udemy – Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins 2025-3

The Mastering Advanced MLOps on GCP-CI/CD Kubernetes Kubeflow course is designed for professionals looking to master advanced MLOps on the Google Cloud Platform. This course provides an in-depth look at the latest techniques and tools needed to build, deploy, and manage scalable machine learning workflows in production environments. During this course, learners will delve into the full MLOps lifecycle, starting with the basics of continuous integration and continuous delivery (CI/CD). You will gain hands-on experience with industry-leading CI/CD tools such as GitHub Actions, GitLab CI, and Jenkins, and learn how to automate testing, deployment, and version control for ML models.

By the end of this course, learners will develop the expertise needed to build, manage, and optimize complex ML pipelines in a cloud-native environment. Hands-on labs and a comprehensive final project provide opportunities to apply these concepts to real-world scenarios, ensuring that you not only understand the theory but can implement solutions in your organization. Whether you’re a machine learning engineer, data scientist, DevOps specialist, or cloud architect, this course will equip you with the skills you need to drive innovation and efficiency in machine learning operations. Get ready to transform your approach to MLOps and harness the full power of GCP with advanced tools like GitHub Actions, GitLab CI, Jenkins, PostgreSQL, Grafana, Kubeflow, and Minikube.

What you will learn

Build and manage robust continuous integration and deployment pipelines using tools like GitHub Action and Jenkins, which are designed for machine learning, GitLab CI/CD.
Use containerization and orchestration tools like Docker, Kubeflow, and Minikube to create scalable, production-ready ML systems on GCP.
Efficiently manage and secure ML data with PostgreSQL while implementing real-time monitoring and visualization dashboards using Grafana.
Apply best practices in scaling, resource management, and security compliance to ensure efficient and secure ML operations in cloud environments.

This course is suitable for people who:

Machine learning engineers and data scientists: Those who want to bridge the gap between model development and scalable deployment.
DevOps and MLOps professionals: People who plan to integrate CI/CD pipelines and container orchestration into ML workflows.
Cloud and Infrastructure Professionals: Professionals looking to deepen their expertise in GCP and related cloud-native tools.
Technical leaders and architects: Decision makers responsible for designing and maintaining robust and scalable ML systems in production.

Course details: Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins

Publisher : Udemy
Teacher : KRISHAI Technologies Private Limited , Sudhanshu Gusain
Language: English
Level : All Levels
Lectures : 112
Duration : 54 hours and 38 minutes

Course headings

Prerequisites for the Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins course

Programming Proficiency: Basic to intermediate experience with programming, particularly in Python, which is widely used in machine learning and scripting for automation.
A basic understanding of machine learning principles, including data preprocessing, model training, and evaluation.
Prior experience with version control systems like Git, which is essential for managing code and collaborating on CI/CD pipelines.
An introductory understanding of cloud platforms (with a focus on GCP) and containerization (eg, Docker) will help you grasp the orchestration concepts covered in the course.

Udemy – Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins 2025-3