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wtf is examples?

jeffwan/examples — explained in plain English

Analysis updated 2026-07-18 · repo last pushed 2020-02-21

Audience · dataComplexity · 4/5DormantSetup · hard

TL;DR

A cookbook of copy-paste-ready Kubeflow tutorials for building and deploying machine learning systems, covering data prep, training, and serving predictions.

Mindmap

mindmap
  root((repo))
    What it does
      End to end ML tutorials
      Component walkthroughs
      Platform demos
    Tech stack
      Kubeflow
      TensorFlow
      PyTorch
      Jupyter notebooks
    Use cases
      Train and serve a model
      Summarize GitHub issues
      Image recognition demo
    Audience
      Data scientists
      ML engineers

Code map

Detail Auto

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Why would anyone build with this?

REASON 1

Follow an end-to-end tutorial to train a model and deploy it as a live prediction service

REASON 2

Build a system that summarizes GitHub issues using natural language processing

REASON 3

Train multiple financial forecasting models in parallel and iterate on them

REASON 4

Learn how to connect Jupyter notebooks to cloud storage and orchestrate training jobs

What's in the stack?

KubeflowTensorFlowPyTorchJupyterSeldon CoreXGBoost

How it stacks up

jeffwan/examples0verflowme/alarm-clock0verflowme/seclists
Language—CSS—
Last pushed2020-02-212022-10-032020-05-03
MaintenanceDormantDormantDormant
Setup difficultyhardeasyeasy
Complexity4/52/51/5
Audiencedatavibe coderops devops

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · hard Time to first run · 1h+

Requires cloud infrastructure and a Kubeflow cluster to run most examples.

Yoink these prompts

Prompt 1
Walk me through one of these Kubeflow examples to train and deploy a model end-to-end.
Prompt 2
Show me how to use the XGBoost example here to train a model on a cloud provider.
Prompt 3
Help me adapt the GitHub issue summarization example to my own text dataset.
Prompt 4
Explain how the image recognition demo monitors training progress automatically.

Frequently asked questions

wtf is examples?

A cookbook of copy-paste-ready Kubeflow tutorials for building and deploying machine learning systems, covering data prep, training, and serving predictions.

Is examples actively maintained?

Dormant — no commits in 2+ years (last push 2020-02-21).

How hard is examples to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is examples for?

Mainly data.

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