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Andrew Ferlitsch
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"## Overview\n",
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"\n",
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"\n",
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"This tutorial demonstrates how to use the Vertex AI SDK for Python to train a custom tabular classification model and perform batch prediction with feature filtering. This means that you can run batch prediction on a list of selected features or exclude a list of features from prediction."
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"This tutorial demonstrates how to use the Vertex AI SDK for Python to train a custom tabular classification model and perform batch prediction with feature filtering. This means that you can run batch prediction on a list of selected features or exclude a list of features from prediction.\n",
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"\n",
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"Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions)."
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]
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"\n",
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"This notebook demonstrates building and deploying a text sentiment classification model by fine-tuing a pre-trained [BERT](https://huggingface.co/bert-base-cased) model using Vertex AI and Pytorch SDK. This example is inspired by the Hugging Face [Token_Classification](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb) and [Run_Glue](https://github.com/huggingface/transformers/blob/v2.5.0/examples/run_glue.py) notebooks. \n",
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"\n",
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"You can find more details about the model at [Hugging Face Hub](https://huggingface.co/bert-base-cased). For more notebooks with the state of the art PyTorch/Tensorflow/JAX, you can explore [Hugging FaceNotebooks](https://huggingface.co/transformers/notebooks.html).\n"
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"You can find more details about the model at [Hugging Face Hub](https://huggingface.co/bert-base-cased). For more notebooks with the state of the art PyTorch/Tensorflow/JAX, you can explore [Hugging FaceNotebooks](https://huggingface.co/transformers/notebooks.html).\n",
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"\n",
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"Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
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]
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},
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"source": [
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"## Overview\n",
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"\n",
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"When you run a distributed training job across multiple nodes using GPUs, communicating gradients between nodes can contribute significant latency. Reduction Server is an all-reduce algorithm that can increase throughput and reduce latency for distributed training. This notebook demonstrates how to run a PyTorch distributed training job with Reduction Server on Vertex AI. The training job is created to fine-tune pretrained model `bert-large-cased` from the Hugging Face Transformers library on the `imdb` dataset for sentiment classification."
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"When you run a distributed training job across multiple nodes using GPUs, communicating gradients between nodes can contribute significant latency. Reduction Server is an all-reduce algorithm that can increase throughput and reduce latency for distributed training. This notebook demonstrates how to run a PyTorch distributed training job with Reduction Server on Vertex AI. The training job is created to fine-tune pretrained model `bert-large-cased` from the Hugging Face Transformers library on the `imdb` dataset for sentiment classification.\n",
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"\n",
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"Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Reduction Server](https://cloud.google.com/blog/topics/developers-practitioners/optimize-training-performance-reduction-server-vertex-ai)."
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"\n",
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"This notebook demonstrates how to create an AutoML Video Classification Model, with a Vertex AI video dataset, and how to serve the model for batch prediction. It requires you provide a bucket where the dataset will be stored.\n",
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"\n",
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"Note: you may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK."
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"Note: you may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n",
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"\n",
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"Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl/training)."
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]
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"\n",
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"This notebook demonstrates how to create a Custom Model using Custom Python Package Training, with a Vertex AI Dataset, and how to serve the model using TensorFlow-Serving Container for online prediction, and batch prediction. It requires you to provide a bucket where the dataset will be stored.\n",
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"\n",
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"Note: You may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n"
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"Note: You may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n",
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"\n",
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"Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
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]
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},
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"This tutorial demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BQML, do an evaluation comparison, for a baseline, before progressing to a custom model.\n",
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"\n",
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"\n",
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"<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />"
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"<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />\n",
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"\n",
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"Learn more about [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
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]
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"\n",
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"Vertex AI provides a algorithm called on [TabNet] (https://arxiv.org/abs/1908.07442). TabNet is an interpretable deep learning architecture for tabular (structured) data, the most common data type among enterprises. TabNet combines the best of two worlds: it is explainable, like simpler tree-based models, and can achieve the high accuracy of complex black-box models and ensembles, meaning it is precise without obscuring how the model works. This makes TabNet well-suited for a wide range of tabular data tasks where model explainability is just as important as accuracy.\n",
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"\n",
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"The goal of the tutorial is to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.\n"
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"The goal of the tutorial is to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.\n",
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"\n",
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"Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
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]
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"\n",
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"TabNet combines the best of two worlds: it is explainable (similar to simpler tree-based models) while benefiting from high performance (similar to deep neural networks). This makes it great for retailers, finance and insurance industry applications such as predicting credit scores, fraud detection and forecasting. \n",
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"\n",
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"TabNet uses a machine learning technique called sequential attention to select which model features to reason from at each step in the model. This mechanism makes it possible to explain how the model arrives at its predictions and helps it learn more accurate models. Thanks to this design, TabNet not only outperforms other neural networks and decision trees but also provides interpretable feature attributions. Releasing TabNet as a First Party Trainer in Vertex AI means you'll be able to easily take advantage of TabNet's architecture and explainability and use it to train models on your own data. "
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"TabNet uses a machine learning technique called sequential attention to select which model features to reason from at each step in the model. This mechanism makes it possible to explain how the model arrives at its predictions and helps it learn more accurate models. Thanks to this design, TabNet not only outperforms other neural networks and decision trees but also provides interpretable feature attributions. Releasing TabNet as a First Party Trainer in Vertex AI means you'll be able to easily take advantage of TabNet's architecture and explainability and use it to train models on your own data. \n",
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"\n",
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"Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview)."
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]
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{
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