Peran dalam fase pengembangan

Ada empat langkah berbeda dalam fase pengembangan sebuah aplikasi MLOps: penyiapan data, pengembangan model, evaluasi model, serta pengujian dan verifikasi.

Anda masih bertanggung jawab mengembangkan sistem peringatan dini untuk Finance 2050. Tim Anda terdiri dari tiga orang: seorang data scientist, seorang data engineer, dan seorang software engineer. Sekarang Anda perlu mendistribusikan tugas kepada anggota proyek.

Latihan ini merupakan bagian dari kursus

MLOps untuk Bisnis

Latihan interaktif langsung

Ubah teori menjadi aksi dengan salah satu latihan interaktif kami

Latihan ini merupakan bagian dari kursus

MLOps untuk Bisnis

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

The first chapter will introduce MLOps and why it is necessary for businesses that want to design, develop, and operate multiple machine learning applications simultaneously. You will learn about the main elements of MLOps, such as scaling and automation, its benefits, and why MLOps remain challenging. You will also explore what it takes to start the MLOps journey both from a technological and managerial perspective.

Exercise 1: What is MLOps?Exercise 2: MLOps: a set of practices to ...Exercise 3: What are characteristics of MLOps?Exercise 4: The business arguments for MLOps Exercise 5: Relevant MLOps fields Exercise 6: How to invest in MLOps Exercise 7: Reasons to invest in MLOps Exercise 8: Laying the foundation for MLOps Exercise 9: MLOps team members Exercise 10: Prerequisites of MLOps Exercise 11: Elements of MLOps

In the second chapter, you’ll learn about the entire MLOps life cycle from design to development, deployment, and operations. You’ll explore why monitoring is essential for productive machine learning applications and why we must regularly re-train machine learning models.

Exercise 1: Penjelasan MLOps dari awal hingga akhir Exercise 2: Sepuluh langkah penting dalam siklus hidup MLOps Exercise 3: Tahapan utama dalam sebuah aplikasi MLOps Exercise 4: Tugas penting di tahapan utama MLOps Exercise 5: Fase perancangan Exercise 6: Menunda tugas pada fase desain Exercise 7: Cetak Biru untuk Sukses: Merancang Proyek MLOps Anda Exercise 8: Fase pengembangan Exercise 9: Tugas pengembangan Exercise 10: Peran dalam fase pengembangan

Latihan Saat Ini

Exercise 11: Pemantauan, pelatihan ulang, dan penggantian aplikasi MLOps Exercise 12: Karakteristik siklus hidup MLOps Exercise 13: Tanggung jawab individu dalam mengembangkan use case MLOps Exercise 14: Siklus hidup MLOps

In the third chapter, you will move from theory to practice and discover the main challenges and risks of deploying machine learning models. You’ll also learn how MLOps teams successfully operate and what management can do to foster successful scaling machine learning.

Exercise 1: Business challenges and risks related to MLOps Exercise 2: What makes MLOps challenging?Exercise 3: Traditional software vs. MLOps Exercise 4: Potential MLOps business risks Exercise 5: How MLOps teams successfully operate Exercise 6: DevOps-style teams Exercise 7: Challenges to implement MLOps Exercise 8: The state of MLOps today Exercise 9: A false statement about MLOps Exercise 10: Reasons that MLOps is challenging

The final chapter will demonstrate how to successfully jumpstart your business's MLOps journey by discussing best practices and pitfalls to avoid. Finally, you’ll examine the different levels of MLOps maturity and conclude the course with a real-life case study about designing, developing, and operating a machine learning application for critical production processes.

Exercise 1: How businesses can become fully MLOps ready Exercise 2: Valid MLOps KPIs Exercise 3: Order deployment maturity Exercise 4: MLOps maturity Exercise 5: MLOps best practices and pitfalls Exercise 6: What do we need to test?Exercise 7: (Not) A best practice Exercise 8: MLOps case study: Increasing profits with MLOps Exercise 9: Key points of the case study Exercise 10: Wrap-up