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 Duration 14 hours

Course Outline

Overview of Kubeflow Framework

  • Reviewing the strategic objectives and architectural design of Kubeflow
  • Examining essential modules and the broader ecosystem
  • Evaluating deployment strategies and platform functionalities

Interaction with the Kubeflow Interface

  • Guiding users through the graphical interface structure
  • Supervising computational notebooks and development environments
  • Configuring data persistence and source integration

Foundations of Kubeflow Pipelines

  • Analyzing workflow architecture and modular component design
  • Constructing automated processes using the Python Software Development Kit
  • Initiating, scheduling, and overseeing automated task execution

Model Training Execution on Kubeflow

  • Implementing distributed computing methodologies
  • Leveraging specific operators such as TFJob and PyTorchJob
  • Controlling computational resources and automatic scaling within Kubernetes

Model Deployment via Kubeflow

  • Reviewing the KFServing and KServe service architectures
  • Deploying models utilizing bespoke execution runtimes
  • Oversight of version revisions, resource scaling, and request distribution

Supervising Machine Learning Processes on Kubernetes

  • Maintaining version control for datasets, models, and outputs
  • Embedding Continuous Integration and Continuous Delivery mechanisms
  • Enforcing security protocols and role-based access controls

Operational Standards for Production Environments

  • Establishing robust and reliable workflow structures
  • Implementing observability metrics and performance monitoring
  • Resolving frequent technical challenges within the Kubeflow environment

Advanced Applications (Optional)

  • Configuring multi-tenant operational frameworks
  • Addressing hybrid cloud and multi-cluster distribution scenarios
  • Expanding Kubeflow capabilities through custom module development

Conclusions and Subsequent Actions

Requirements

  • Demonstrated knowledge of containerized application architectures
  • Proficiency in fundamental command-line operational procedures
  • Working familiarity with Kubernetes operational concepts

Target Participants

  • Machine Learning Engineers and Practitioners
  • Data Science professionals
  • DevOps personnel initiating Kubeflow integration

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