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Course Outline
Perkenalan
Ikhtisar Kubeflow Fitur dan Komponen
- Kontainer, manifes, dll.
Ikhtisar Saluran Pipa Machine Learning.
- Pelatihan, pengujian, penyetelan, penerapan, dll.
Menyebarkan Kubeflow ke Cluster Kubernetes.
- Mempersiapkan lingkungan eksekusi (kluster pelatihan, kluster produksi, dll.)
- Mengunduh, menginstal, dan menyesuaikan.
Menjalankan Alur Machine Learning pada Kubernetes
- Membangun saluran pipa TensorFlow.
- Membangun saluran pipa PyTorch.
Memvisualisasikan Hasilnya
- Mengekspor dan memvisualisasikan metrik saluran
Menyesuaikan Lingkungan Eksekusi
- Menyesuaikan tumpukan untuk beragam infrastruktur
- Meningkatkan penerapan Kubeflow.
Berjalan Kubeflow di Cloud Publik
- AWS, Microsoft Azure, Google Cloud Platform
Mengelola Alur Kerja Produksi
- Berjalan dengan metodologi GitOps
- Menjadwalkan pekerjaan
- Memunculkan buku catatan Jupyter
Penyelesaian masalah
Ringkasan dan Kesimpulan
Requirements
- Keakraban dengan sintaks Python.
- Pengalaman dengan Tensorflow, PyTorch, atau framework pembelajaran mesin lainnya
- Akun penyedia cloud publik (opsional)
Hadirin
- Pengembang
- Ilmuwan data
28 Hours
Testimonials (1)
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.
