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Category: What is TensorFlow Extended

Written by Amy Schouten2023-02-18

Model deployment and predictions – Vertex AI Custom Model Hyperparameter and Deployment

Click on the imported model to see versions of the model (multiple versions of the model can be imported under the same model). In our case version 1 will be the only version of the model and it will be default as shown in the Figure 5.26 and follow the steps mentioned: Step 1: Version

Written by Amy Schouten2022-11-16

Image creation – Vertex AI Custom Model Hyperparameter and Deployment-2

Additional permissions . Components of TFX . Google Certification Exams . Pipeline comparison . What is TensorFlow Extended Article

Step 5: Hyperparameter tuning The next step is for hyperparameters. Follow the corresponding steps to enable and configure the hyperparameter tuning for model training, as shown in the Figure 5.15: Figure 5.15: Configuring hyperparameter tuning Follow the corresponding points: Refer to Table 5.1 and add the details of remaining four parameters: Parameter name Type

Written by Amy Schouten2022-04-25

Objectives – Vertex AI Custom Model Hyperparameter and Deployment

Exams of Google . Google Certification Exams . Model building code . Pipeline comparison . What is TensorFlow Extended Article

Introduction In the previous chapter, we started working on the custom model building on Google Cloud Platform (GCP) using Vertex AI components. In this chapter, we will see how to create a custom job with hyperparameter tuning, and how to initiate the training job using Python code. Structure In this chapter, we will cover the

Written by Amy Schouten2022-03-21

Completion of custom model training job – Vertex AI Workbench and Custom Model Training

Additional permissions . Google Certification Exams . Model building code . Pipeline comparison . What is TensorFlow Extended Article

Based on the complexity of the model, model training might take few mins to few hours. Once the model training is completed it will be listed under the training section and also the model will be available in the model section. Training job will be listed under training section of vertex AI as shown in

Written by Amy Schouten2022-02-24

Image creation – Vertex AI Workbench and Custom Model Training-2

Additional permissions . Components of TFX . Google Certification Exams . Model building code . Pipeline comparison . What is TensorFlow Extended Article

Step 2: Selecting training method Train new model page will appear as shown in Figure 4.27. Follow the steps mentioned in the figure to configure training method: Figure 4.27: Training method selection for custom model Step 3: Model details Select the model details to train the model as shown in Figure 4.28: Figure 4.28: Model

Written by Amy Schouten2022-01-30

Image creation – Vertex AI Workbench and Custom Model Training-1

Additional permissions . Components of TFX . Data for model training . Google Certification Exams . Pipeline comparison . What is TensorFlow Extended Article

Now that we have completed the creation of Dockerfile and Python code for the model building. We can proceed with image creation. Step 1: Image creation using docker build Type the following commands as shown in Figure 4.19 for image creation: IMAGE_URL=”gcr.io/$PROJECT_ID/price:v1” docker build ./ -t $IMAGE_URL Figure 4.19: Image creation process It will take

Written by Amy Schouten2021-12-24

Model building – Vertex AI Workbench and Custom Model Training

Additional permissions . Components of TFX . Data for model training . Exams of Google . Google Certification Exams . What is TensorFlow Extended Article

Once the Dockerfile is created, we need to work on the Python code for the model building. The task is to build a regression model to predict the electricity prices. Python code will be copied into the container and submitted for training job. Let us go back to the terminal of the workbench and follow

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