Mengapa mendokumentasikan metode pelabelan?

Anda ditugaskan menulis dokumentasi untuk proses pelatihan model Anda. Sebagai bagian dari dokumentasi ini, Anda menyadari bahwa data yang awalnya Anda gunakan tidak memiliki label yang tepat dan Anda tidak tahu bagaimana hal itu bisa terjadi karena tidak ada dokumentasi. Terus terang, Anda beruntung dapat menyadarinya!

Untuk mengatasinya, Anda membentuk tim beranggotakan orang-orang yang memiliki pengetahuan mendalam tentang subjek untuk membuat variabel respons yang dikaitkan dengan benar untuk tugas supervised. Anda menyimpulkan bahwa penting untuk mendokumentasikan proses ini agar orang lain mengetahui bagaimana label dibuat dan dapat mengkritiknya jika diperlukan.

Anda meluangkan waktu untuk menuliskan bagaimana Anda mendapatkan label tersebut, tetapi Anda merasa ada alasan lain mengapa Anda menuliskan semua ini. Apa alasan lain untuk mendokumentasikan metode pelabelan data untuk tugas supervised?

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: Mengadopsi pola pikir MLOps Exercise 2: Apa aspek kunci dari MLOps?Exercise 3: Apa itu technical debt?Exercise 4: Menulis kode ML yang mudah dipelihara Exercise 5: Pengendali versi Exercise 6: Organisasi kode Exercise 7: Menulis dokumentasi ML yang efektif Exercise 8: Mengapa mendokumentasikan metode pelabelan?

Latihan Saat Ini

Exercise 9: Praktik terbaik MLOps

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: Packaging ML models Exercise 2: Goal of serialization and containerization Exercise 3: Model packaging order Exercise 4: Scalability Exercise 5: Scaling Exercise 6: Model complexity Exercise 7: Categorizing scaling techniques Exercise 8: Automation Exercise 9: Benefits of continuous delivery Exercise 10: CI/CD steps Exercise 11: Putting ML models into production

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!