Custom model batch prediction with feature filtering
Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then run a batch prediction job by including or excluding a list of features.
The steps performed include:
- Create a Vertex AI custom `TrainingPipeline` for training a model.
- Train a TensorFlow model.
- Send batch prediction job.
Learn more about Vertex AI Batch Prediction.
Get started with Custom Prediction Routine (CPR)
Learn how to use Custom Prediction Routine for `Vertex AI Predictions`.
The steps performed include:
- Write a custom data preprocessor.
- Train the model.
- Build a custom scikit-learn serving container with custom data preprocessing using the Custom Prediction Routine model server.
- Test the model serving container locally.
- Upload and deploy the model serving container to Vertex AI Endpoint.
- Make a prediction request.
- Build a custom scikit-learn serving container with custom predictor (post-processing) using the Custom Prediction Routine model server.
- Implement custom predictor.
- Test the model serving container locally.
- Upload and deploy the model serving container to Vertex AI Endpoint.
- Make a prediction request.
- Build a custom scikit-learn serving container with custom predictor and HTTP request handler using the Custom Prediction Routine model server.
- Implement a custom handler.
- Test the model serving container locally.
- Upload and deploy the model serving container to Vertex AI Endpoint.
- Make a prediction request.
- Customize the Dockerfile for a custom scikit-learn serving container with custom predictor and HTTP request handler using the Custom Prediction Routine model server.
- Implement a custom Dockerfile.
- Test the model serving container locally.
- Upload and deploy the model serving container to Vertex AI Endpoint.
- Make a prediction request.
Learn more about Custom prediction routines.
Vertex AI LLM and streaming prediction
Learn how to use Vertex AI LLM to download pretrained LLM model, make predictions and finetuning the model.
The steps performed include:
- Load a pretrained text generation model.
- Make a non-streaming prediction
- Load a pretrained text generation model, which supports streaming.
- Make a streaming prediction
- Load a pretrained chat model.
- Do a local interactive chat session.
- Do a batch prediction with a text generation model.
- Do a batch prediction with a text embedding model.
Learn more about Vertex AI Language Models.
Serving PyTorch image models with prebuilt containers on Vertex AI
Learn how to package and deploy a PyTorch image classification model using a prebuilt Vertex AI container with TorchServe for serving online and batch predictions.
The steps performed include:
- Download a pretrained image model from PyTorch
- Create a custom model handler
- Package model artifacts in a model archive file
- Upload model for deployment
- Deploy model for prediction
- Make online predictions
- Make batch predictions
Learn more about Pre-built containers for prediction.
Train and deploy PyTorch models with prebuilt containers on Vertex AI
Learn how to build, train and deploy a PyTorch image classification model using prebuilt containers for custom training and prediction.
The steps performed include:
- Package training application into a Python source distribution
- Configure and run training job in a prebuilt container
- Package model artifacts in a model archive file
- Upload model for deployment
- Deploy model using a prebuilt container for prediction
- Make online predictions
Vertex AI SDK 2.0 Vertex AI Remote Prediction
Learn to use `Vertex AI SDK 2.
The steps performed include:
- Download and split the dataset
- Perform transformations as a Vertex AI remote training.
- For scikit-learn, PyTorch, TensorFlow, PyTorch Lightning
- Train the model remotely.
- Uptrain the pretrained model remotely.
- Evaluate both the pretrained and uptrained model.
- Make a prediction remotely