MLOps (Machine Learning Operations) Fundamentals
4.7 /5- Offered byCoursera
MLOps (Machine Learning Operations) Fundamentals at Coursera Overview
Duration | 16 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
MLOps (Machine Learning Operations) Fundamentals at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 8 of 9 in the Preparing for Google Cloud Certification: Machine Learning Engineer
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 16 hours to complete
- English Subtitles: English
MLOps (Machine Learning Operations) Fundamentals at Coursera Course details
- This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
- 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 <<<
MLOps (Machine Learning Operations) Fundamentals at Coursera Curriculum
Welcome to MLOps Fundamentals
Course Introduction
How to download course resources
How to Send Feedback
Data Scientists? Pain Points
Machine Learning Lifecycle
MLOps Architecture and TensorFlow Extended Components
Why and When to Employ MLOps
Introduction
Introduction to Containers
Containers and Container Images
Lab Intro
Lab solution
Introduction to Kubernetes
Introduction to Google Kubernetes Engine
Compute Options Detail
Kubernetes Concepts
The Kubernetes Control Plane
Google Kubernetes Engine Concepts
Lab Intro
Lab solution
Deployments
Ways to Create Deployments
Services and Scaling
Updating Deployments
Rolling Updates
Blue-Green Deployments
Canary Deployments
Managing Deployments
Lab Intro
Jobs and CronJobs
Parallel Jobs
CronJobs
Introduction to Containers
Containers and Container Images
Introduction to Kubernetes
Introduction to Google Kubernetes Engine
Containers and Kubernetes in Google Cloud
Kubernetes Concepts
The Kubernetes Control Plane
Google Kubernetes Engine Concepts
Deployments
Updating Deployments
Jobs
Introduction to AI Platform Pipelines
Overview
Introduction to AI Platform Pipelines
Concepts
When to use
Ecosystem
Getting Started with Google Cloud and Qwiklabs
Lab Solution
AI Platform Pipelines
System and concepts overview
Create a reproducible dataset
Implement a tunable model
Build and push a training container
Train and tune the model
Serve and query the model
Lab Intro
Lab Solution
Training, Tuning and Serving on AI Platform
Kubeflow Pipelines on AI Platform
System and concept overview
Describing a Kubeflow Pipeline with KF DSL
Pre-built components
Lightweight Python Components
Custom components
Compile, upload and Run
Lab Intro
Lab Solution
Kubeflow Pipelines on AI Platform
Concept Overview
Cloud Build Builders
Cloud Build Configuration
Cloud Build Triggers
Lab Intro
CI/CD for a Kubeflow Pipeline
Summary
Other courses offered by Coursera
Student Forum
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