DataCamp

MLOps Deployment and Life Cycling

Name: MLOps Deployment and Life Cycling
Availability: InStock
Rating: 4.8 (900 reviews)

4.8(900)12k students

Korshub's take

Managing the Production Machine Learning Lifecycle

Deploying champion models into production requires robust engineering strategies to avoid technical debt and maintain long-term reliability. This advanced course covers the post-experimentation lifecycle: building, deploying, monitoring, and maintaining machine learning applications in real-world production environments.

Who Is This Course For?

Tailored for experienced data engineers and machine learning practitioners who build, deploy, and govern production models using structured MLOps frameworks.

Key Takeaways

Clean Deployment Code: Learn to write maintainable ML code and build automated training pipelines that minimize technical debt.
Deployment Strategies & APIs: Understand different deployment environments and methods for replacing existing production models via APIs.
Production Monitoring & Governance: Track critical performance metrics, manage covariate drift, handle model feedback, and enforce governance rules.

About this course

MLOps Deployment and LifeCycling

Explore the modern MLOps framework, including the lifecycle and deployment of machine learning models. In this course, you’ll learn to write ML code that minimizes technical debt, discover the tools you’ll need to deploy and monitor your models, and examine the different types of environments and analytics you’ll encounter.

Learn About the MLOps Lifecycle

After you’ve collected, prepared, and labeled your data, run numerous experiments on different models, and proven your concept with a champion model, it’s time for the next steps. Build. Deploy. Monitor. Maintain. That is the life cycle of your model once it's destined for production. That is the Ops part of MLOps. This course will show you how to navigate the second chapter of your model's journey to value delivery, setting the benchmark for many more to come. You’ll start by exploring the MLOps lifecycle, discovering the importance of MLOps and the key functional components for model development, deployment, monitoring, and maintenance.

Develop ML Code for Deployment

Next, you’ll learn how to develop models for deployment and how to write effective ML code, leverage tools, and train ML pipelines. As you progress, you’ll cover how to deploy your models, exploring different deployment environments and when to use them. You’ll also develop strategies for replacing existing production models and examine APIs.

Learn How to Monitor Your Models

As you complete the course, you’ll discover the crucial performance metrics behind monitoring and maintaining your ML models. You’ll learn about drift monitoring in production, as well as model feedback, updates, and governance. By the time you’re finished, you’ll understand how you can use MLOps lifecycle to deploy your own models in production.