Quando il tempo conta - ma non troppo

Hai imparato come la latenza accettabile del tuo servizio di Machine Learning influisca sulla scelta della modalità di serving da implementare.

A volte gli utenti possono aspettare giorni, persino settimane. Altre volte, un secondo è troppo.

Più bassa è la latenza attesa, maggiori diventano le sfide di engineering e i costi del servizio. Quindi evita l’over-engineering e allinea il design del tuo servizio di ML a ciò che gli utenti richiedono e sono disposti a pagare.

Per esempio, supponi di creare un servizio di ML per analizzare e riassumere grandi documenti .pdf. Se gli utenti ti dicono che vorrebbero ricevere i risultati del servizio entro 5 minuti dalla richiesta, la modalità di serving più ragionevole per questo caso d’uso sarebbe:

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Deployment e ciclo di vita in MLOps

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Deployment e ciclo di vita in MLOps

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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: The modern MLOps framework Exercise 2: ML workflows Exercise 3: MLOps benefits Exercise 4: Life-cycling stages Exercise 5: App vs. model Exercise 6: Decommissioning Exercise 7: The model life cycle: recap Exercise 8: MLOps components Exercise 9: Automated sequence Exercise 10: Stores and registries Exercise 11: DevOps or 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: Modalità di serving Exercise 2: Offline o online?Exercise 3: Quando il tempo conta - ma non troppo

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Exercise 4: Creare l'API Exercise 5: Client-server Exercise 6: Funzionalità dell'API Exercise 7: Progressione del deployment e test Exercise 8: Che test è?Exercise 9: Progressione tra gli ambienti Exercise 10: Test per ambiente Exercise 11: Strategie di deployment del modello Exercise 12: Una strategia di deployment adatta Exercise 13: Ordine di rischio Exercise 14: L'ombra dell'ombra

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