MLOps Specialist Minidegree

Earner of this Minidegree Certification can work with ML Projects, Automate Workflow and can setup the CI/CD Pipelines.

My Journey - MLOps Specialist

1

Course – Foundations of ML, GenAI And Agentic AI

1 AI Trinity Credit

Approx 12 Hours of Learning Content

What you'll learn

  • Essentials of Machine Learning
  • Introduction to Machine Learning Algorithms
  • Introduction to GenAI and LLMs
  • Introduction to Agentic AI

Check out this course at https://schoolofdevops.com/programs/mastering-python-for-ai-ml/

2

Course – MLOps Bootcamp

2 AI Trinity Credit

Approx 45 Hours of Learning Content

What you'll learn

  • Build end-to-end Machine Learning pipelines with MLOps best practices
  • Understand and implement ML lifecycle from data engineering to model deployment
  • Set up MLFlow for experiment tracking and model versioning
  • Package and serve models using FastAPI and Docker
  • Automate workflows using GitHub Actions for CI pipelines
  • Deploy inference infrastructure on Kubernetes using KIND
  • Use Streamlit for building lightweight ML web interfaces
  • Learn GitOps-based CD pipelines using ArgoCD
  • Learn GitOps-based CD pipelines using ArgoCD
  • Serve models in production using Seldon Core
  • Monitor models with Prometheus and Grafana for production insights
  • Understand handoff workflows between Data Science, ML Engineering, and DevOps
  • Build foundational skills to transition from DevOps to MLOps roles

Check out this course at https://schoolofdevops.com/programs/mlops-bootcamp

3

MLOps Specialist

Earner of this Speciality has the following capabilities

  • Build end-to-end Machine Learning pipelines with MLOps best practices
  • Understand and implement ML lifecycle from data engineering to model deployment
  • Set up MLFlow for experiment tracking and model versioning
  • Package and serve models using FastAPI and Docker
  • Automate workflows using GitHub Actions for CI pipelines
  • Deploy inference infrastructure on Kubernetes using KIND
  • Use Streamlit for building lightweight ML web interfaces
  • Learn GitOps-based CD pipelines using ArgoCD
  • Learn GitOps-based CD pipelines using ArgoCD
  • Serve models in production using Seldon Core
  • Monitor models with Prometheus and Grafana for production insights
  • Understand handoff workflows between Data Science, ML Engineering, and DevOps
  • Build foundational skills to transition from DevOps to MLOps roles