Machine Learning Operations (MLOps): Getting Started

This course is part of Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Instructor: Google Cloud Training

What you'll learn

  •   Identify and use core technologies required to support effective MLOps.
  •   Adopt the best CI/CD practices in the context of ML systems.
  •   Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
  •   Implement reliable and repeatable training and inference workflows.
  •   

    • Skills you'll gain

  •   Automation
  •   Continuous Deployment
  •   Machine Learning Software
  •   Continuous Integration
  •   MLOps (Machine Learning Operations)
  •   Software Development Methodologies
  •   DevOps
  •   Continuous Monitoring
  •   Google Cloud Platform
  •   Machine Learning

    • There are 4 modules in this course

    This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

    Employing Machine Learning Operations

    Vertex AI and MLOps on Vertex AI

    Summary

    Explore more from Machine Learning

    Machine Learning Operations (MLOps) with Vertex AI: Manage Features

    MLOps | Machine Learning Operations

    Machine Learning Operations (MLOps): Getting Started - Português Brasileiro

    Operationalizing ML Models: MLOps for Scalable AI

    ©2025  ementorhub.com. All rights reserved