Online or onsite, instructor-led live Kubeflow training courses demonstrate through interactive hands-on practice how to use Kubeflow to build, deploy, and manage machine learning workflows on Kubernetes.
Kubeflow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Kubeflow training can be carried out locally on customer premises in Aberdeen or in NobleProg corporate training centers in Aberdeen.
The Berry Street Centre occupies a three-storey 1950s building, which has recently been refurbished to a high specification. ...
The Berry Street Centre occupies a three-storey 1950s building, which has recently been refurbished to a high specification. It is in a prominent and convenient location in the heart of Aberdeen's busy and thriving retail and business area and close to all amenities. The off-shore oil capital of Europe, Aberdeen is known for its outward looking, pioneering spirit. Over the last 30 years, the city has reinvented itself from a regional port dependent upon fishing, farming and tourism, to become the centre of the UK's energy industry and one of Europe's most enterprising regions. It boasts the second highest rate of business formation and the highest concentration of knowledge based businesses in Scotland and is a founding member of the World Energy Cities Partnership. Other important sectors in the city's economy include tourism and distribution, the public sector, financial services, manufacture, agriculture and food processing, IT and construction.
This instructor-led, live training in Aberdeen (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.
This instructor-led, live training in Aberdeen (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.
This instructor-led, live training in Aberdeen (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.
This instructor-led, live training in Aberdeen (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.
This instructor-led, live training in Aberdeen (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.
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.
This instructor-led, live training in Aberdeen (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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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™
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