Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins | Udemy [Update 03/2025]
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Simply streamline ML pipelines with Kubernetes, GitLab CI, Jenkins, Prometheus, 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.
This Beginner to Advanced MLOps Course covers a wide range of technologies and tools essential for building, deploying, and automating ML models in production.
Technologies & Tools Used Throughout the Course
Experiment Tracking & Model Management: MLFlow, Comet-ML, TensorBoard
Data & Code Versioning: DVC, Git, GitHub, GitLab
CI/CD Pipelines & Automation: Jenkins, ArgoCD, GitHub Actions, GitLab CI/CD, CircleCI
Cloud & Infrastructure: GCP (Google Cloud Platform), Minikube, Google Cloud Run, Kubernetes
Deployment & Containerization: Docker, Kubernetes, FastAPI, Flask
Data Engineering & Feature Storage: PostgreSQL, Redis, Astro Airflow, PSYCOPG2
ML Monitoring & Drift Detection: Prometheus, Grafana, Alibi-Detect
API & Web App Development: FastAPI, Flask, ChatGPT, Postman, SwaggerUI
How These Tools & Techniques Help
Experiment Tracking & Model Management
Helps in logging, comparing, and tracking different ML model experiments.
MLFlow & Comet-ML provide centralized tracking of hyperparameters and performance metrics.
Data & Code Versioning
Ensures reproducibility by tracking data changes over time.
DVC manages large datasets, and GitHub/GitLab maintains version control for code and pipelines.
CI/CD Pipelines & Automation
Automates ML workflows from model training to deployment.
Jenkins, GitHub Actions, GitLab CI/CD, and ArgoCD handle continuous integration & deployment.
Cloud & Infrastructure
GCP provides scalable infrastructure for data storage, model training, and deployment.
Minikube enables Kubernetes testing on local machines before deploying to cloud environments.
Deployment & Containerization
Docker containerizes applications, making them portable and scalable.
Kubernetes manages ML deployments for high availability and scalability.
Data Engineering & Feature Storage
PostgreSQL & Redis store structured and real-time ML features.
Airflow automates ETL pipelines for seamless data processing.
ML Monitoring & Drift Detection
Prometheus & Grafana visualize ML model performance in real-time.
Alibi-Detect helps in identifying data drift and model degradation.
API & Web App Development
FastAPI & Flask create APIs for real-time model inference.
ChatGPT integration enhances chatbot-based ML applications.
SwaggerUI & Postman assist in API documentation & testing.
This course ensures a complete hands-on approach to MLOps, covering everything from data ingestion, model training, versioning, deployment, monitoring, and CI/CD automation to make ML projects production-ready and scalable.
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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