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Andrew Ferlitsch
2022-02-08 12:57:22 -08:00
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@@ -35,19 +35,104 @@ The third stage in MLOps is formalization to develop an automated pipeline proce
[Get Started with Kubeflow pipelines](get_started_with_kubeflow_pipelines.ipynb)
```
The steps performed include:
- Building KFP lightweight Python function components.
- Assembling and compiling KFP components into a pipeline.
- Executing a KFP pipeline using Vertex AI Pipelines.
- Building sequential, parallel, multiple output components.
- Building control flow into pipelines.
```
[Get Started with BQ and TFDV components](get_started_with_bq_tfdv_pipeline_components.ipynb)
```
The steps performed include:
- Build and execute a pipeline component for creating a Vertex AI Tabular Dataset from a BigQuery table.
- Build and execute a pipeline component for generating TFDV statistics and schema from a Vertex AI Tabular Dataset.
- Execute a Vertex AI pipeline.
```
[Get Started with Dataflow components](get_started_with_dataflow_pipeline_components.ipynb)
```
The steps performed include:
- Build an Apache Beam data pipeline.
- Encapsulate the Apache Beam data pipeline with a Dataflow component in a Vertex AI pipeline.
- Execute a Vertex AI pipeline.
```
[Get Started with Vertex AI AutoML components](get_started_with_automl_pipeline_components.ipynb)
```
The steps performed include:
- Construct a pipeline for:
- Training a Vertex AI AutoML trained model.
- Test the serving binary with a batch prediction job.
- Deploying a Vertex AI AutoML trained model.
- Execute a Vertex AI pipeline.
```
[Get Started with Vertex AI Custom Training components](get_started_with_custom_training_pipeline_components.ipynb)
```
The steps performed include:
- Construct a pipeline for:
- Training a Vertex AI custom trained model.
- Test the serving binary with a batch prediction job.
- Deploying a Vertex AI custom trained model.
- Execute a Vertex AI pipeline.
```
[Get Started with Vertex AI Hyperparameter Tuning components](get_started_with_hpt_pipeline_components.ipynb)
```
- Construct a pipeline for:
- Training BigQuery ML model.
- Evaluating the BigQuery ML model.
- Exporting the BigQuery ML model.
- Importing the BigQuery ML model to a Vertex AI model.
- Deploy the Vertex AI model.
- Execute a Vertex AI pipeline.
- Make a prediction with the deployed Vertex AI model.
```
[Get Started with BQML components](get_started_with_bqml_pipeline_components.ipynb)
```
The steps performed include:
- Construct a pipeline for:
- Hyperparameter tune/train a custom model.
- Retrieve the tuned hyperparameter values and metrics to optimize.
- If the metrics exceed a specified threshold.
- Get the location of the model artifacts for the best tuned model.
- Upload the model artifacts to a `Vertex AI Model` resource.
- Execute a Vertex AI pipeline.
```
### E2E Stage Example
[Stage 3: Formalization](mlops_formalization.ipynb)
```
The steps performed include:
- Obtain resources from the experimentation stage.
- Baseline model.
- Dataset schema/statistics for baseline model.
- Formalize a data preprocessing pipeline.
- Extract columns/rows from BigQuery table to local BigQuery table.
- Use Tensorflow Data Validation library to determine statistics, schema, and features.
- Use Dataflow to preprocess the data.
- Create a Vertex AI Dataset.
- Formalize a build model architecture pipeline.
- Create the Vertex AI Model base model.
- Formalize a training pipeline.
```