Hangzhou Foreign Economy & Trade Plaza

8/F, 8/F, Block B, No. 468 Yan'an Road, Gongshu District, Hangzhou, China, 310006
The Hangzhou Foreign Economy & Trade Plaza Centre is in the heart of Hangzhou's central business district. The area i...

Subcategories (2)

Image for MLflow category
MLflow
Image for Kubeflow category
Kubeflow

Explore Our Courses

MLflow

 21 Hours
This instructor-led, live training in Hangzhou (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process. By the end of this training, participants will be able to:
  • Install and configure MLflow and related ML libraries and frameworks.
  • Appreciate the importance of trackability, reproducability and deployability of an ML model
  • Deploy ML models to different public clouds, platforms, or on-premise servers.
  • Scale the ML deployment process to accommodate multiple users collaborating on a project.
  • Set up a central registry to experiment with, reproduce, and deploy ML models.
Read more...

Kubeflow

 35 Hours
This instructor-led, live training in Hangzhou (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.
Read more...

Kubeflow Fundamentals

 28 Hours
This instructor-led, live training in Hangzhou (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.
Read more...

Kubeflow on AWS

 28 Hours
This instructor-led, live training in Hangzhou (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.
Read more...

Kubeflow on Azure

 28 Hours
This instructor-led, live training in Hangzhou (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.
Read more...

Kubeflow on GCP

 28 Hours
This instructor-led, live training in Hangzhou (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP). By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
  • Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
  • 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 GCP services to extend an ML application.
Read more...

Kubeflow on IBM Cloud

 28 Hours
This instructor-led, live training in Hangzhou (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS). By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
  • Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
  • 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 IBM Cloud services to extend an ML application.
Read more...

Kubeflow on OpenShift

 28 Hours
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
  • By the end of this training, participants will be able to:
  • Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
  • Use OpenShift to simplify the work of initializing a Kubernetes cluster.
  • 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.
  • Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
Read more...

MLOps: CI/CD for Machine Learning

 35 Hours
This instructor-led, live training in Hangzhou (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization. By the end of this training, participants will be able to:
  • Install and configure various MLOps frameworks and tools.
  • Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
  • Prepare, validate and version data for use by ML models.
  • Understand the components of an ML Pipeline and the tools needed to build one.
  • Experiment with different machine learning frameworks and servers for deploying to production.
  • Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
Read more...

Last Updated:

Testimonials (2)

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.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Course - Kubeflow

Upcoming Courses

Other regions in China

Other Countries

These courses are also available in other countries

Consulting

MLOps Consulting
Online MLOps training in Hangzhou, MLOps training courses in Hangzhou, Weekend MLOps courses in Hangzhou, Evening MLOps training in Hangzhou, MLOps instructor-led in Hangzhou, MLOps coaching in Hangzhou, MLOps instructor-led in Hangzhou, Weekend MLOps training in Hangzhou, MLOps instructor in Hangzhou, MLOps private courses in Hangzhou, MLOps boot camp in Hangzhou, MLOps trainer in Hangzhou, MLOps on-site in Hangzhou, MLOps classes in Hangzhou, MLOps one on one training in Hangzhou, Evening MLOps courses in Hangzhou, Online MLOps training in Hangzhou