Langkah-langkah CI/CD

Bayangkan Anda adalah bagian dari tim data scientist dan developer di sebuah perusahaan yang telah mengembangkan sistem rekomendasi bertenaga AI untuk konten yang dipersonalisasi. Sistem ini sudah diterapkan di lingkungan produksi dan membantu pengguna menemukan artikel, video, dan media lain yang relevan berdasarkan preferensi serta riwayat penelusuran mereka. Untuk menjaga efektivitas dan relevansinya, tim Anda harus terus memperbarui model Machine Learning yang digunakan dalam sistem rekomendasi tersebut.

Perusahaan menggunakan proses Continuous Integration dan Continuous Deployment (CI/CD) untuk menyederhanakan pengembangan, pelatihan, dan penerapan model. Tugas Anda adalah mengikuti langkah-langkah dalam siklus CI/CD, dengan mengingat bahwa model sudah berada di produksi, dan memastikan model tetap mutakhir serta dioptimalkan kinerjanya.

Latihan ini adalah bagian dari kursus

Mengembangkan Model Machine Learning untuk Produksi

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Mengembangkan Model Machine Learning untuk Produksi

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.

Exercise 1: Adopting an MLOps mindset Exercise 2: What is a key aspect of MLOps?Exercise 3: What is technical debt?Exercise 4: Writing maintainable ML code Exercise 5: Version control Exercise 6: Code organization Exercise 7: Writing effective ML documentation Exercise 8: Why document labeling methods?Exercise 9: MLOps best practices

In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.

Exercise 1: Designing reproducible experiments Exercise 2: ML reproducibility Exercise 3: Identifying Key Aspects for Reproducibility and Reliability Exercise 4: Sorting Reliability Elements Exercise 5: Sorting Aspects of Reproducibility and Documentation Practices Exercise 6: Feature engineering Exercise 7: Classifying Feature Engineering Goals & Techniques Exercise 8: Identifying Data Manipulation and Transformation Techniques Exercise 9: Feature selection Exercise 10: Data and model versioning Exercise 11: Model store Exercise 12: Versioning training data Exercise 13: Ensuring reproducibility

In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.

Exercise 1: Membungkus model ML Exercise 2: Tujuan serialisasi dan kontainerisasi Exercise 3: Urutan pengemasan model Exercise 4: Skalabilitas Exercise 5: Penskalaan Exercise 6: Kompleksitas model Exercise 7: Mengategorikan teknik penskalaan Exercise 8: Otomatisasi Exercise 9: Manfaat continuous delivery Exercise 10: Langkah-langkah CI/CD

Latihan Saat Ini

Exercise 11: Menerapkan model ML ke produksi

In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.

Exercise 1: Model reliability Exercise 2: Categorizing Testing Strategies for ML Pipeline Readiness and Staleness Exercise 3: Addressing Model Staleness in Machine Learning Pipelines Exercise 4: Testing data Exercise 5: Picking the right test Exercise 6: Consistency check Exercise 7: Testing models Exercise 8: Predicting customer lifetime value Exercise 9: Classifying fairness Exercise 10: Testing machine learning pipelines Exercise 11: Congratulations!