Mlops-On-Gcp

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mlops-on-gcp is an Open Source & Models tool that provides hands-on labs and code samples for MLOps. It demonstrates best practices for implementing and operationalizing machine learning workflows on Google Cloud Platform.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is mlops-on-gcp?

mlops-on-gcp is a GitHub repository by Google Cloud Platform dedicated to MLOps. It offers a comprehensive collection of hands-on labs and code samples, showcasing best practices and effective patterns for implementing and operationalizing production-grade machine learning workflows on Google Cloud Platform. The repository is structured into two main sections: mini-workshops for instructor-led learning and code samples that illustrate various ML Engineering topics. This resource is ideal for developers and data scientists looking to deploy and manage machine learning models efficiently within the Google Cloud ecosystem, providing practical guidance and examples for continuous training, model serving, and more.

Best used for

Ideal for developers and data scientists who need to learn MLOps, implement machine learning workflows, and deploy ML models on Google Cloud Platform. Especially valuable for understanding best practices and patterns for production-grade ML systems.

Common actions

learn MLOps

implement ML workflows

deploy ML models

operationalize ML

collaborationopen-sourceworkflowslow-code/no-codeautomated workflow"AI Agents"face swappingdeepfakegithub copilot

Capabilities

Key features

Hands-on labs
Code samples
MLOps best practices
Continuous training examples
Model serving patterns

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of content is available in the mlops-on-gcp repository?

The repository contains hands-on labs for instructor-led mini-workshops and code samples. These resources demonstrate design patterns and best practices for various ML Engineering topics, helping users implement and operationalize machine learning workflows on Google Cloud Platform.

Is mlops-on-gcp suitable for beginners in MLOps?

While it provides hands-on labs, the content focuses on best practices and production-grade workflows, suggesting an intermediate to advanced technical skill level. Users should have some familiarity with machine learning concepts and Google Cloud Platform.

What specific MLOps topics are covered in the code samples?

The repository includes examples for topics such as continuous training, model serving, on-demand processing, and skew detection. It aims to cover a variety of ML Engineering aspects relevant to deploying and managing models effectively on GCP.

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Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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