Online Kubeflow Training in US

Kubeflow is an open-source platform designed to streamline the development, training, and deployment of machine learning workloads on Kubernetes. This instructor-led, live training (online or onsite) is aimed at professionals at the beginner to intermediate levels who wish to build reliable ML workflows using Kubeflow for government applications. Upon completion of this training, attendees will gain the skills to: - Navigate the Kubeflow ecosystem and its core components. - Build reproducible workflows with Kubeflow Pipelines. - Run scalable training jobs on Kubernetes. - Serve machine learning models efficiently using Kubeflow Serving. **Format of the Course** - Guided presentations and collaborative discussions. - Hands-on labs with real Kubeflow components. - Practical exercises to build end-to-end ML workflows. **Course Customization Options** - Customized versions of this training can be arranged to align with your team’s technology stack and project requirements for government use.

This instructor-led, live training in US (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government. By the end of this training, participants will be able to: - Install and configure Kubeflow both 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 comprehensive machine learning pipelines across various architectures and cloud environments. - Utilize Kubeflow to spawn and manage Jupyter notebooks. - Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server for government. By the end of this training, participants will be able to: - Install and configure Kubernetes, Kubeflow, and other necessary software on AWS. - Utilize EKS (Elastic Kubernetes Service) to simplify the initialization of a Kubernetes cluster on AWS. - Develop and deploy a Kubernetes pipeline for automating and managing ML models in production environments. - Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel. - Leverage other AWS managed services to enhance an ML application.

This instructor-led, live training in [location] (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to the Azure cloud for government. By the end of this training, participants will be able to: - Install and configure Kubernetes, Kubeflow, and other necessary software on Azure. - Use Azure Kubernetes Service (AKS) to streamline the initialization of a Kubernetes cluster on Azure. - Create and deploy a Kubernetes pipeline for automating and managing ML models in production environments. - Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel. - Leverage other Azure managed services to extend an ML application, ensuring alignment with public sector workflows and governance.

This instructor-led, live training in [location] (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government use. By the end of this training, participants will be able to: - Install and configure Kubeflow both on-premise and in the cloud. - Build, deploy, and manage ML workflows using Docker containers and Kubernetes. - Run comprehensive machine learning pipelines across various architectures and cloud environments. - Utilize Kubeflow to create and manage Jupyter notebooks. - Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.