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.
