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

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.

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

Description
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 CourseExperiment Tracking & Model Management: MLFlow, Comet-ML, TensorBoardData & Code Versioning: DVC, Git, GitHub, GitLabCI/CD Pipelines & Automation: Jenkins, ArgoCD, GitHub Actions, GitLab CI/CD, CircleCICloud & Infrastructure: GCP (Google Cloud Platform), Minikube, Google Cloud Run, KubernetesDeployment & Containerization: Docker, Kubernetes, FastAPI, FlaskData Engineering & Feature Storage: PostgreSQL, Redis, Astro Airflow, PSYCOPG2ML Monitoring & Drift Detection: Prometheus, Grafana, Alibi-DetectAPI & Web App Development: FastAPI, Flask, ChatGPT, Postman, SwaggerUIHow These Tools & Techniques HelpExperiment Tracking & Model ManagementHelps in logging, comparing, and tracking different ML model experiments.MLFlow & Comet-ML provide centralized tracking of hyperparameters and performance metrics.Data & Code VersioningEnsures reproducibility by tracking data changes over time.DVC manages large datasets, and GitHub/GitLab maintains version control for code and pipelines.CI/CD Pipelines & AutomationAutomates ML workflows from model training to deployment.Jenkins, GitHub Actions, GitLab CI/CD, and ArgoCD handle continuous integration & deployment.Cloud & InfrastructureGCP provides scalable infrastructure for data storage, model training, and deployment.Minikube enables Kubernetes testing on local machines before deploying to cloud environments.Deployment & ContainerizationDocker containerizes applications, making them portable and scalable.Kubernetes manages ML deployments for high availability and scalability.Data Engineering & Feature StoragePostgreSQL & Redis store structured and real-time ML features.Airflow automates ETL pipelines for seamless data processing.ML Monitoring & Drift DetectionPrometheus & Grafana visualize ML model performance in real-time.Alibi-Detect helps in identifying data drift and model degradation.API & Web App DevelopmentFastAPI & 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.