Bootcamp de DevOps a MLOps: Transición hacia la Ingeniería P

Course Overview

• Course Title: Bootcamp de DevOps a MLOps: Transición hacia la Ingeniería P
• Instructor: Gourav Shah
• Target Audience:
  • DevOps Engineers
  • Infrastructure Professionals
  • Machine Learning Engineers
• Prerequisites:
  • Basic knowledge of DevOps, including Docker, Git, and CI/CD
  • Basic experience with command line and terminal
  • Ideally, previous experience working with Kubernetes

Curriculum Highlights

• Key Topics Covered:
  • Machine learning project lifecycle
  • MLflow configuration and usage
  • Data engineering techniques
  • Model packaging with FastAPI
  • Deployment with Docker and Kubernetes
  • Building visual interfaces with Streamlit
  • Automating ML pipelines with GitHub Actions
  • Managing container images with DockerHub
  • Implementing models with Seldon Core
  • Monitoring with Prometheus and Grafana
  • GitOps with ArgoCD
  • Integrating DevOps practices into MLOps workflows
• Key Skills Learned:
  • Understanding the complete lifecycle of a machine learning project
  • Configuring and using MLflow for experiment tracking
  • Applying data engineering techniques in Jupyter notebooks
  • Packaging ML models using FastAPI
  • Deploying models with Docker and Kubernetes
  • Creating visual interfaces with Streamlit
  • Automating ML pipelines with GitHub Actions
  • Managing container images with DockerHub
  • Implementing production models with Seldon Core
  • Monitoring production models using Prometheus and Grafana
  • Applying GitOps for continuous delivery using ArgoCD
  • Integrating DevOps practices into MLOps workflows

Course Format

• Duration: 9 hours on-demand video
• Format: Self-paced online course
• Resources:
  • 1 article
  • Access on mobile and TV
  • Certificate of completion