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. Atlanta onsite live Kubeflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
GA, Atlanta - Colony Square
1201 Peachtree St. NE,, Atlanta, united states, 30361
The venue is located in the heart of midtown Atlanta, on the second floor of the 400 building on the corner of 14th Street and Peachtree Street, which is connected to a well-known mall. The office building, which is in the cultural arts district of the city, was one of the first mixed-use developments in the south when it was constructed in the 1970s.
GA, Atlanta - Proscenium
1170 Peachtree Street, Atlanta, United States, 30309
The venue is located across the street from Colony Square in the same building as Yahoo Inc.
GA, Decatur - One West Court Square
1 W Ct Square #750, Decatur, United States, 30030
The venue is located on One West Court Square right next door to the DeKalb History Center Museum.
Atlanta, GA - One Hartsfield
100 Hartsfield Centre Parkway, Atlanta, United States, 30354
The venue is located just up the road from the Concourse Atlanta Airport and next door to the Renaissance Concourse Atlanta Airport Hotel.
GA, Atlanta - Downtown 260 Peachtree
260 Peachtree St NW, Atlanta, united states, 30303
The Regus 260 Peachtree office space in Atlanta is located at 260 Peachtree Street in the prestigious HUB zone.
This instructor-led, live training in Atlanta (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 Atlanta (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.
This instructor-led, live training in Atlanta (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 Atlanta (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
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