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Category: Components of TFX

Written by Amy Schouten2022-09-20

Model building code – Vertex AI Custom Model Hyperparameter and Deployment

Once the Dockerfile is created, we need to work on the python code for the model building. The task is to build a classification model. 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 the given steps to create

Written by Amy Schouten2022-08-21

Data for building custom model – Vertex AI Custom Model Hyperparameter and Deployment

Components of TFX . Data for model training . Exams of Google . Google Certification Exams . Model building code Article

For this exercise data is downloaded from Kaggle (link is provided further down) and the dataset is listed under CC0: Public domain licenses. Data contains various measurements from EEG and the state of the eye is captured via the camera. 1 indicates closed eye and 0 indicates open eye. https://www.kaggle.com/datasets/robikscube/eye-state-classification-eeg-dataset hpo_vertex-ai bucket is created under

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

Written by Amy Schouten2021-11-18

Creation of Dockerfile – Vertex AI Workbench and Custom Model Training

Additional permissions . Components of TFX . Data for model training . Exams of Google . Google Certification Exams Article

A Dockerfile is a text file that is read from top to bottom by Docker. It comprises of a series of instructions that instruct Docker how to build the Docker image. A Dockerfile is a recipe for creating Docker images and executing a separate build command that generates the Docker image from that recipe. We

Written by Amy Schouten2021-10-23

Data for building custom model – Vertex AI Workbench and Custom Model Training

Components of TFX . Data for model training . Exams of Google . Google Certification Exams Article

For this exercise data is downloaded from Kaggle (link is provided below) and the dataset is listed under CC0:Public domain licenses. Data contains electricity prices for a data center and factors which may influence the price. https://www.kaggle.com/datasets/salilchoubey/electrity-prices custom_model_ele_prices bucket is created under us-centra1 (single region) and .csv file is uploaded from to the bucket as

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