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 Duration 28 hours

Course Outline

Introduction

Overview of Kubeflow Capabilities and Architectural Components

  • Containerization, manifest specifications, and related artifacts

Overview of Machine Learning Pipeline Structures

  • Training, evaluation, tuning, deployment, and lifecycle management

Deploying Kubeflow to a Kubernetes Cluster

  • Preparing the execution environment (training cluster, production cluster, etc.)
  • Acquisition, installation, and configuration procedures

Executing Machine Learning Pipelines on Kubernetes

  • Developing a TensorFlow pipeline
  • Developing a PyTorch pipeline

Visualizing and Reporting Results

  • Exporting and visualizing pipeline performance metrics

Customizing the Execution Environment

  • Adapting the technology stack for diverse infrastructure requirements
  • Upgrading Kubeflow deployments

Running Kubeflow on Public Cloud Services

  • Amazon Web Services, Microsoft Azure, Google Cloud Platform

Managing Production Workflows

  • Implementing GitOps methodologies
  • Scheduling automated jobs
  • Instantiating Jupyter notebooks

Troubleshooting and Resolution

Summary and Conclusion

Requirements

  • Demonstrated proficiency with Python syntax and structures
  • Practical experience with TensorFlow, PyTorch, or equivalent machine learning frameworks
  • Access to a public cloud provider account (optional requirement)

Target Audience

  • Software developers
  • Data scientists

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

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Related Courses

Kubeflow Essentials: Build, Train & Serve with Kubernetes

 14 Hours

Kubeflow constitutes an open-source infrastructure intended to optimize the development, training, and deployment of machine learning operations on Kubernetes for government and public sector use.

This live, instructor-facilitated session, available remotely or in-person, targets professionals with foundational to intermediate experience who seek to establish secure and reliable machine learning workflows for government applications.

Upon successful completion of this instruction, participants will acquire the competency to:

  • Operate within the Kubeflow ecosystem and utilize its core modules.
  • Develop consistent and reproducible workflows utilizing Kubeflow Pipelines.
  • Execute scalable training operations on Kubernetes infrastructure.
  • Deliver machine learning models with high efficiency using the Kubeflow Serving framework.

Instructional Methodology

  • Structured presentations and facilitated collaborative dialogue.
  • Practical laboratory sessions utilizing actual Kubeflow components.
  • Applied exercises focused on constructing complete machine learning workflows.

Adaptation Options for Course Content

  • Tailored variations of this training program may be developed to accommodate specific agency technology stacks and mission requirements.
Read more...

Related Categories

Kubeflow
Kubeflow