Explore Our Courses

Kubeflow

 35 Hours
Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ditujukan untuk pengembang dan ilmuwan data yang ingin membangun, menerapkan, dan mengelola alur kerja pembelajaran mesin di Kubernetes. Pada akhir pelatihan ini, peserta akan mampu:
  • Instal dan konfigurasikan Kubeflow di lokasi dan di cloud menggunakan AWS EKS (Layanan Elastis Kubernetes).
  • Bangun, terapkan, dan kelola alur kerja ML berdasarkan Docker container dan Kubernetes.
  • Jalankan seluruh alur pembelajaran mesin pada beragam arsitektur dan lingkungan cloud.
  • Menggunakan Kubeflow untuk menelurkan dan mengelola notebook Jupyter.
  • Bangun pelatihan ML, penyesuaian hyperparameter, dan penyajian beban kerja di berbagai platform.
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Kubeflow Fundamentals

 28 Hours
Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ditujukan untuk pengembang dan ilmuwan data yang ingin membangun, menerapkan, dan mengelola alur kerja pembelajaran mesin di Kubernetes. Pada akhir pelatihan ini, peserta akan mampu:
  • Instal dan konfigurasikan Kubeflow di lokasi dan di cloud.
  • Bangun, terapkan, dan kelola alur kerja ML berdasarkan Docker container dan Kubernetes.
  • Jalankan seluruh alur pembelajaran mesin pada beragam arsitektur dan lingkungan cloud.
  • Menggunakan Kubeflow untuk menelurkan dan mengelola notebook Jupyter.
  • Bangun pelatihan ML, penyesuaian hyperparameter, dan penyajian beban kerja di berbagai platform.
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Kubeflow on AWS

 28 Hours
This instructor-led, live training in Indonesia (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server. By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on AWS.
  • Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
  • Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
  • Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
  • Leverage other AWS managed services to extend an ML application.
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Kubeflow on Azure

 28 Hours
This instructor-led, live training in Indonesia (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud. By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on Azure.
  • Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
  • Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
  • Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
  • Leverage other AWS managed services to extend an ML application.
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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.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM

Course - Kubeflow

Upcoming Courses (Minimal 5 peserta)

Kubeflow on AWS

2025-05-26 09:30
28 hours
Grand Slipi Tower
80600000 IDR (Online)
80610908 IDR (Classroom)

Kubeflow on AWS

2025-06-09 09:30
28 hours
The City Tower
80600000 IDR (Online)
80610908 IDR (Classroom)

Kubeflow

2025-06-23 09:30
35 hours
Centennial Tower
136350000 IDR (Online)
136363635 IDR (Classroom)

Kubeflow on AWS

2025-07-07 09:30
28 hours
Grand Slipi Tower
80600000 IDR (Online)
80610908 IDR (Classroom)

Kubeflow on AWS

2025-07-21 09:30
28 hours
The City Tower
80600000 IDR (Online)
80610908 IDR (Classroom)

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Kubeflow Consulting
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