Luring atau daring?

Dalam pelajaran ini, Anda mempelajari bahwa salah satu perbedaan paling mendasar antara mode penyajian adalah apakah eksekusinya dijadwalkan atau dipicu oleh peristiwa dan permintaan pengguna.

Pendekatan akhir Anda harus ditentukan oleh kebutuhan pengguna, tanpa kompleksitas yang tidak perlu dan tidak menambah nilai.

Sebagai contoh, jika Anda membangun model untuk menghasilkan prediksi cuaca yang ingin diterima pengguna hanya sekali sehari, setiap malam, pilihan yang paling masuk akal adalah:

Latihan ini adalah bagian dari kursus

Penyebaran dan Siklus Hidup MLOps

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Penyebaran dan Siklus Hidup MLOps

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4.8+

Mulai Kursus Gratis

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: Mode penyajian Exercise 2: Luring atau daring?

Latihan Saat Ini

Exercise 3: Ketika waktu penting — sedikit Exercise 4: Membangun API Exercise 5: Klien-server Exercise 6: Fungsionalitas API Exercise 7: Perkembangan deployment dan pengujian Exercise 8: Tes yang mana?Exercise 9: Progres melalui lingkungan Exercise 10: Pengujian per lingkungan Exercise 11: Strategi penerapan model Exercise 12: Strategi penerapan yang tepat Exercise 13: Urutan risiko Exercise 14: Bayangan dari bayangan

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