Back tostdlib
Activity

ML ops and monitoring

Establish practices for deploying and monitoring machine learning models

Design and implement a comprehensive framework for deploying, monitoring, and maintaining machine learning models in production.

110 minutes
creation

Overview

Design and implement a comprehensive framework for deploying, monitoring, and maintaining machine learning models in production.

Learning objectives

  • Define ML deployment pipeline
  • Establish model monitoring practices
  • Create drift detection systems
  • Build model governance framework

Instructions

Create a robust framework for managing ML models in production.

Steps

1

Map lifecycle

25 minutes

Map ML model lifecycle

2

Design pipeline

30 minutes

Design deployment pipeline

3

Implement monitoring

25 minutes

Implement monitoring and alerting

4

Create governance

20 minutes

Create governance processes

5

Document practices

10 minutes

Document best practices

Pro tips

  • •Monitor both model and data quality
  • •Implement gradual rollouts
  • •Maintain model versioning
  • •Plan for model retraining

Example outcome

A production-ready ML operations framework that ensures model reliability, performance monitoring, and systematic improvements.

Explore more resources

Check out the full stdlib collection for more frameworks, templates, and guides to accelerate your technical leadership journey.