Mandalay Classroom

77th x 78th Street, MaHarAungMyay Township, Mandalay, mm, 11101

The Centre located within 2 km of Sri Ganesh Temple and 2.1 km of Ma Soe Yein Nu Kyaung Monastery, 2.3 km from Mahar Thatkyarthiha Pagoda. Cultural Museum & Library is 2.4 km from the hotel and Mandalay University is 2.4 km away, Jade Market is 2.6 km from the accommodation. The nearest airport is Mandalay International Airport, 38 km from Gold Leaf Hotel. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.

Subcategories (1)

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Kubeflow

Explore Our Courses

DeepSeek: Advanced Model Optimization and Deployment

 14 Hours
This instructor-led, live training in Mandalay (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices. By the end of this training, participants will be able to:
  • Optimize DeepSeek models for efficiency, accuracy, and scalability.
  • Implement best practices for MLOps and model versioning.
  • Deploy DeepSeek models on cloud and on-premise infrastructure.
  • Monitor, maintain, and scale AI solutions effectively.
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Kubeflow

 35 Hours
This instructor-led, live training in Mandalay (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes. By the end of this training, participants will be able to:
  • Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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Kubeflow Fundamentals

 28 Hours
This instructor-led, live training in Mandalay (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes. By the end of this training, participants will be able to:
  • Install and configure Kubeflow on premise and in the cloud.
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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Kubeflow on AWS

 28 Hours
This instructor-led, live training in Mandalay (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 Mandalay (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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MLOps for Azure Machine Learning

 14 Hours
This instructor-led, live training in (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices. By the end of this training, participants will be able to:
  • Build reproducible workflows and machine learning models.
  • Manage the machine learning lifecycle.
  • Track and report model version history, assets, and more.
  • Deploy production ready machine learning models anywhere.
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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

MLOps for Azure Machine Learning

2025-05-06 09:30
14 hours
Mandalay Classroom
168661 LKR (Online)
188661 LKR (Classroom)

Kubeflow on AWS

2025-05-20 09:30
28 hours
Mandalay Classroom
421322 LKR (Online)
461322 LKR (Classroom)

Kubeflow

2025-06-09 09:30
35 hours
Mandalay Classroom
421652 LKR (Online)
471652 LKR (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-06-17 09:30
14 hours
Mandalay Classroom
168661 LKR (Online)
188661 LKR (Classroom)

Kubeflow Fundamentals

2025-07-01 09:30
28 hours
Mandalay Classroom
337322 LKR (Online)
377322 LKR (Classroom)

Kubeflow

2025-07-21 09:30
35 hours
Mandalay Classroom
421652 LKR (Online)
471652 LKR (Classroom)

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