MLOps Deployment and LifeCyclingExplore 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 LifecycleAfter 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 DeploymentNext, 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 ModelsAs 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.
MLOps in a NutshellFree
This chapter gives a high-level overview of MLOps principles and framework components important for deployment and life cycling.
Develop for Deployment
This chapter is dedicated to all the considerations we need to make already in the development phase, in order to ensure a smooth ride when we reach the operations. Our ultimate goal is to explain how to train the model using MLOps best practices and build a model package that enables smooth deployment, reproducibility and post-deployment monitoring.Deployment-driven development50 xpTesting your machine learning model50 xpBest time to start deploying50 xpProfiling, versioning, and feature stores50 xpFeature store properties100 xpProfiling and feature store benefits100 xpEnsuring reproducibility50 xpModel build pipelines in CI/CD50 xpDifferent pipelines100 xpModel build pipeline properties100 xpCI/CD integration100 xpModel packaging50 xpModel formats50 xpFull package100 xp
Deploy and Run
This chapter deals with critical model operations questions such as: - What are the different ways in which we can serve our models? - What is an API, and what are its key functionalities? - How do we thoroughly test our service before making it available to the end users? - How do we update models in production without service disturbance? You will learn about batch prediction, real-time prediction, input and output data validation, unit testing, integration testing, canary deployment, and much more.Serving modes50 xpOffline or online?50 xpWhen time matters - a bit50 xpBuilding the API50 xpClient-server50 xpAPI functionalities50 xpDeployment progression and testing50 xpWhich test is it?50 xpProgression through environments100 xpTests per environment100 xpModel deployment strategies50 xpA fitting deployment strategy50 xpOrder of risk100 xpShadow of the shadow50 xp
Monitor and Maintain
This final chapter is dedicated to monitoring and maintaining ML services after they are deployed, as well as to model governance. You will cover crucial concepts such as verification latency, covariate shift, concept drift, human-in-the-loop systems, and more.Monitoring ML services50 xpShift vs drift100 xpLatency50 xpAlready?50 xpMonitoring and alerting50 xpThe monitoring system100 xpAlerting50 xpModel maintenance50 xpData-centric vs Model-centric100 xpHuman-in-the-Loop50 xpModel governance50 xpElements of governance100 xpStages of governance100 xpRisk classification50 xpWrap up50 xp
Nemanja RadojkovićSee More
Senior Data Scientist @ Euroclear
Nemanja's areas of expertise include Machine Learning, Text Mining and Computer Vision. He is currently a Senior Data Scientist at Euroclear, where he helps businesses to harvest the power of their data. He has worked with industries as varied as manufacturing, life sciences & healthcare, and transportation services & infrastructure.