Waktu terbaik untuk mulai melakukan deployment

Anda mendapat tugas mengembangkan mesin rekomendasi untuk layanan streaming yang benar-benar baru.

Membangun model untuk kasus penggunaan ini akan menjadi proses yang menantang, dan proses eksperimen serta penyetelan saja sudah bisa menyita seluruh perhatian Anda.

Lalu, suatu hari, setelah beberapa bulan, Anda berhasil! Kinerja yang dibutuhkan tercapai, jadi mari kita deploy karya ini!

Baik, tapi di mana? Bagaimana?

Realitas pun menyadarkan Anda bahwa terlalu fokus pada pengembangan dapat membuat persiapan deployment luput dari perhatian. Hal ini bisa membuat semua upaya Anda sia-sia, karena Anda tidak bisa begitu saja melakukan deployment model apa pun ke platform apa pun; keduanya harus selaras. Pada akhirnya Anda memerlukan tiga bulan tambahan untuk pengembangan guna membuat paket model yang benar-benar baru, yang siap untuk deployment.

Namun kini Anda lebih bijak dan tahu bahwa momen terbaik untuk mulai menyiapkan model dan aplikasi ML Anda untuk deployment 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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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: Pengembangan berorientasi deployment Exercise 2: Menguji model machine learning Anda Exercise 3: Waktu terbaik untuk mulai melakukan deployment

Latihan Saat Ini

Exercise 4: Profiling, versioning, dan feature store Exercise 5: Properti feature store Exercise 6: Manfaat profiling dan feature store Exercise 7: Memastikan reproduksibilitas Exercise 8: Pipa pembangunan model dalam CI/CD Exercise 9: Pipeline yang berbeda Exercise 10: Properti model build pipeline Exercise 11: Integrasi CI/CD Exercise 12: Pengemasan model Exercise 13: Format model Exercise 14: Paket lengkap

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