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Course Outline
Perkenalan
- Machine Learning model vs perangkat lunak tradisional
Ikhtisar DevOps Alur Kerja
Ikhtisar Machine Learning Alur Kerja
ML sebagai Data Kode Plus
Komponen Sistem ML
Studi Kasus: Aplikasi Penjualan Forecasting.
Access Data
Memvalidasi Data
Transformasi Data
Dari Pipa Data ke Pipa ML
Membangun Model Data
Melatih Model
Memvalidasi Model
Pelatihan Model Reproduksi
Menerapkan Model
Melayani Model Terlatih ke Produksi
Menguji Sistem ML
Orkestrasi Pengiriman Berkelanjutan
Memantau Model
Pembuatan Versi Data
Mengadaptasi, Menskalakan, dan Mempertahankan MLOps Platform
Penyelesaian masalah
Ringkasan dan Kesimpulan
Requirements
- Pemahaman tentang siklus pengembangan perangkat lunak
- Pengalaman membangun atau bekerja dengan model Machine Learning.
- Keakraban dengan pemrograman Python.
Hadirin
- Insinyur ML
- DevOps insinyur
- Insinyur data
- Insinyur infrastruktur
- Pengembang perangkat lunak
35 Hours
Testimonials (3)
Ada banyak latihan praktik yang dipandu dan dibantu oleh pelatih.
Aleksandra - Fundacja PTA
Course - Mastering Make: Advanced Workflow Automation and Optimization
Machine Translated
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
