Uitfaseren

Je hebt geleerd dat vroeg of laat alle modellen het einde van hun levenscyclus bereiken, waarna ze moeten worden "uitgefaseerd" en vervangen door nieuwe.

Dit heet decommissioning en moet om goede redenen gebeuren, waarvan we er in de laatste video een paar hebben genoemd.

In tegenstelling tot die redenen: welke van de volgende zou GEEN goede reden zijn om je huidige model uit te faseren?

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MLOps-deployments en levenscyclus

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Deze oefening maakt deel uit van de cursus

MLOps-deployments en levenscyclus

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This chapter gives a high-level overview of MLOps principles and framework components important for deployment and life cycling.

Exercise 1: Het moderne MLOps-framework Exercise 2: ML-workflows Exercise 3: Voordelen van MLOps Exercise 4: Fasen van de life cycle Exercise 5: App vs. model Exercise 6: Uitfaseren

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Exercise 7: De levenscyclus van het model: samenvatting Exercise 8: MLOps-componenten Exercise 9: Geautomatiseerde reeks Exercise 10: Stores en registries Exercise 11: DevOps of MLOps?

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.

Exercise 1: Deployment-driven development Exercise 2: Testing your machine learning model Exercise 3: Best time to start deploying Exercise 4: Profiling, versioning, and feature stores Exercise 5: Feature store properties Exercise 6: Profiling and feature store benefits Exercise 7: Ensuring reproducibility Exercise 8: Model build pipelines in CI/CD Exercise 9: Different pipelines Exercise 10: Model build pipeline properties Exercise 11: CI/CD integration Exercise 12: Model packaging Exercise 13: Model formats Exercise 14: Full package

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.

Exercise 1: Serving modes Exercise 2: Offline or online?Exercise 3: When time matters - a bit Exercise 4: Building the API Exercise 5: Client-server Exercise 6: API functionalities Exercise 7: Deployment progression and testing Exercise 8: Which test is it?Exercise 9: Progression through environments Exercise 10: Tests per environment Exercise 11: Model deployment strategies Exercise 12: A fitting deployment strategy Exercise 13: Order of risk Exercise 14: Shadow of the shadow

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.

Exercise 1: Monitoring ML services Exercise 2: Shift vs drift Exercise 3: Latency Exercise 4: Already?Exercise 5: Monitoring and alerting Exercise 6: The monitoring system Exercise 7: Alerting Exercise 8: Model maintenance Exercise 9: Data-centric vs Model-centric Exercise 10: Human-in-the-Loop Exercise 11: Model governance Exercise 12: Elements of governance Exercise 13: Stages of governance Exercise 14: Risk classification Exercise 15: Wrap up