Mastering Advanced MLOps on GCP-CI/CD, Kubernetes Kubeflow

Simply streamline ML pipelines with GitHub Actions, GitLab CI, Jenkins, PostgreSQL, Grafana, Kubeflow & Minikube on GCP.
What you'll learn
Build and manage robust continuous integration and deployment pipelines using tools like GitHub Action and Jenkins tailored for machine learning s, GitLab CI/CD
Utilize containerization and orchestration tools such as 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.
Requirements
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 (e.g., Docker) will help you grasp the orchestration concepts covered in the course.
Description
This course is designed for professionals looking to master advanced MLOps on Google Cloud Platform. It offers an in-depth exploration of the latest techniques and tools required to build, deploy, and manage scalable machine learning workflows in production environments.Throughout the course, learners will dive into the full lifecycle of MLOps, starting with the fundamentals of continuous integration and continuous delivery (CI/CD). You'll gain hands-on experience with industry-leading CI/CD tools such as GitHub Actions, GitLab CI, and Jenkins, learning how to automate testing, deployment, and version control for your ML models.Key components of the course include:CI/CD Pipelines: Understand the principles of CI/CD and learn how to implement automated workflows tailored for machine learning projects. You will configure pipelines that not only deploy code but also handle model training, testing, and validation seamlessly.Data Management with PostgreSQL: Learn best practices for integrating and managing databases in your ML projects. This section covers how to use PostgreSQL for storing and versioning data, ensuring data integrity and efficient retrieval during model training and inference.Monitoring & Visualization with Grafana: Gain insights into setting up real-time monitoring dashboards with Grafana. You'll learn how to track model performance, system health, and resource utilization to maintain optimal operations in your ML systems.Containerization & Orchestration: Delve into containerization using Docker and master advanced orchestration tools with Kubeflow and Minikube. These sessions focus on deploying containerized ML workflows on GCP, enabling you to build scalable, production-grade systems that can easily be managed and scaled.Advanced GCP Integration: Explore the robust ecosystem of GCP services tailored for machine learning and data operations. You will understand how to integrate these services into your MLOps pipelines for enhanced performance, security, and scalability.By the end of this course, learners will have developed the expertise to build, manage, and optimize complex ML pipelines in a cloud-native environment. Practical labs and a comprehensive capstone project provide opportunities to apply these concepts in real-world scenarios, ensuring that you not only understand the theory but can also implement solutions in your own organization.Whether you are a Machine Learning Engineer, Data Scientist, DevOps specialist, or Cloud Architect, this course equips you with the skills necessary to drive innovation and efficiency in machine learning operations. Prepare to transform your approach to MLOps and leverage the full power of GCP combined with state-of-the-art tools like GitHub Actions, GitLab CI, Jenkins, PostgreSQL, Grafana, Kubeflow, and Minikube.
Who this course is for
Machine Learning Engineers & Data Scientists: Those who want to bridge the gap between model development and scalable deployment.
DevOps & MLOps Practitioners: Individuals aiming to integrate CI/CD pipelines and container orchestration into ML workflows.
Cloud & Infrastructure Specialists: Professionals seeking to deepen their expertise in GCP and related cloud-native tools.
Technical Leaders & Architects: Decision-makers responsible for designing and maintaining robust, scalable ML systems in production.
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