Original inspiration for this repository came from here: https://github.com/elvinzhusiyu/vapit
Other very good sources for looking for examples:
GCP AI Platform Sample Repository
GCP Vertex Pipeline examples
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Who is the audience for this article? Data Scientists, Developers, AI/ML practitioners
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What problem(s) are we solving for that audience with this article? Provide an end-to-end ML pipeline from concept to production with usable templates
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What action(s) do we want the audience to take once they’re done reading this article? Clone the sample repository and try running the templates in their own GCP environments
(~150 words)
- Highlight business value of end-to-end ML pipeline on GCP in prod vs running locally in Jupyter notebook
- Highlight template structure for different frameworks (Tensorflow, XGBoost, Scikit-learn, AutoML, etc.)
- Link to frameworks, link to GCP tools, link to the public repo
(~150 words)
- Cover end-to-end process on a high level
- Data store → Prep → HPT → Training → Deploy → Prediction
- Orchestration aspect
(~300 words)
(~300 words)
- Open Jupyterlab on the Notebooks instance and clone the workshop repo
- Open sklearn-prebuilt-container/sklearn-pb-ctr-setup.ipynb
- Talk through each component
(~300 words)
- TBD
(~150 words)
This is not an official Google product but sample code provided for an educational purpose.
Copyright 2021 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.