AR, Little Rock - Regions Center

10800 Financial Centre Pkway #125, Little Rock, united states, 72211

Experience a professional, modern workspace at the Regions Center office at 400 West Capitol Avenue in the heart of d...

AZ, Phoenix - Central Ave Suite 1400

40 North Central Ave, Suite 1400, Pheonix, united states, 85004
The center is conveniently located in downtown Phoenix and in just walking distance to numerous restaurants, shops, and busin...

Explore Our Courses

Kubeflow

35 Hours
This instructor-led, live training in Arizona (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 on AWS

28 Hours
This instructor-led, live training in Arizona (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 Arizona (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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Kubeflow on GCP

28 Hours
This instructor-led, live training in Arizona (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.
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Kubeflow on IBM Cloud

28 Hours
This instructor-led, live training in Arizona (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.
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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.
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Kubeflow Fundamentals

28 Hours
This instructor-led, live training in Arizona (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...

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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™
Course - Kubeflow

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