Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

MLOps Essentials: Model Development and Integration

via LinkedIn Learning

Overview

Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.

Syllabus

Introduction
  • Getting started with MLOps
  • Scope and prerequisites
1. Introduction to MLOps
  • Machine learning life cycle
  • Unique challenges with ML
  • What is DevOps?
  • What is MLOps?
  • Principles of MLOps
  • When to start MLOps?
2. Requirements and Design
  • Selecting ML projects
  • Creating requirements
  • Designing the ML workflow
  • Assembling the team
  • Choosing tools and technologies
3. Data Processing and Management
  • Managed data pipelines
  • Automated data validation
  • Managed feature stores
  • Data versioning
  • Data governance
  • Tools and technologies for data processing
4. Continuous Training
  • Managed training pipelines
  • Creating data labels
  • Experiment tracking
  • AutoML
  • Tools and technologies for training
5. Model Management
  • Model versioning
  • Model registry
  • Benchmarking models
  • Model life cycle management
  • Tools and technologies for model management
6. Continuous Integration
  • Solution integration pipelines
  • Notebook to software
  • Solution integration patterns
  • Best practices for solution integration
Conclusion
  • Continuing on with MLOps

Taught by

Kumaran Ponnambalam

Reviews

4.5 rating at LinkedIn Learning based on 383 ratings

Start your review of MLOps Essentials: Model Development and Integration

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.