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.