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Title: Use RunInference for Generative AI  |  Dataflow ML  |  Google Cloud Documentation

Open Graph Title: Use RunInference for Generative AI  |  Dataflow ML  |  Google Cloud Documentation

Opengraph URL: https://docs.cloud.google.com/dataflow/docs/notebooks/run_inference_generative_ai

direct link

Domain: cloud.google.com


Hey, it has json ld scripts:
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Overview https://cloud.google.com/dataflow/docs
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About Dataflow MLhttps://cloud.google.com/dataflow/docs/machine-learning/ml-about
ML workflow orchestrationhttps://cloud.google.com/dataflow/docs/machine-learning/ml-data
All Dataflow ML notebookshttps://cloud.google.com/dataflow/docs/machine-learning/notebooks
Process data with MLTransformhttps://cloud.google.com/dataflow/docs/machine-learning/ml-preprocess-data
Compute and apply vocabularyhttps://cloud.google.com/dataflow/docs/notebooks/compute_and_apply_vocab
Scale datahttps://cloud.google.com/dataflow/docs/notebooks/scale_data
Process ML data using Dataflow and Cloud Storage FUSEhttps://cloud.google.com/dataflow/docs/machine-learning/ml-process-dataflow-fuse
Generate embeddings with MLTransformhttps://cloud.google.com/dataflow/docs/machine-learning/ml-generate-embeddings
Generate text embeddings with Vertex AIhttps://cloud.google.com/dataflow/docs/notebooks/vertex_ai_text_embeddings
Generate text embeddings with Hugging Facehttps://cloud.google.com/dataflow/docs/notebooks/huggingface_text_embeddings
Vector embedding ingestion with Apache Beam and AlloyDBhttps://cloud.google.com/dataflow/docs/notebooks/alloydb_product_catalog_embeddings
Embedding ingestion and vector search with Apache Beam and BigQueryhttps://cloud.google.com/dataflow/docs/notebooks/bigquery_vector_ingestion_and_search
RunInference transform best practiceshttps://cloud.google.com/dataflow/docs/machine-learning/runinference-best-practices
Use RunInference with PyTorchhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_pytorch
Use RunInference with scikit-learnhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_sklearn
Use RunInference with TensorFlowhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_tensorflow
Use RunInference with Vertex AIhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_vertex_ai
Use RunInference with vLLMhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_vllm
Use RunInference with TFX Basic Shared Librarieshttps://cloud.google.com/dataflow/docs/notebooks/run_inference_tensorflow_with_tfx
Use RunInference for generative AIhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_generative_ai
Use RunInference with Hugging Face modelshttps://cloud.google.com/dataflow/docs/notebooks/run_inference_huggingface
Use RunInference with TensorFlow Hub modelshttps://cloud.google.com/dataflow/docs/notebooks/run_inference_with_tensorflow_hub
Build a custom model handlerhttps://cloud.google.com/dataflow/docs/notebooks/run_custom_inference
Run inference with a remote modelhttps://cloud.google.com/dataflow/docs/notebooks/custom_remote_inference
About ensemble modelshttps://cloud.google.com/dataflow/docs/machine-learning/ml-multi-model
Ensemble model tutorialhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_multi_model
Run models by cohorthttps://cloud.google.com/dataflow/docs/notebooks/per_key_models
About automatic model refreshhttps://cloud.google.com/dataflow/docs/machine-learning/ml-model-refresh
Automatic model refresh tutorialhttps://cloud.google.com/dataflow/docs/notebooks/automatic_model_refresh
Evaluate and compare modelshttps://cloud.google.com/dataflow/docs/notebooks/tfma_beam
Anomaly Detection with statistical methodshttps://cloud.google.com/dataflow/docs/notebooks/anomaly_detection_zscore
Use Gemma models with Dataflowhttps://cloud.google.com/dataflow/docs/machine-learning/gemma
Run inference with a Gemma modelhttps://cloud.google.com/dataflow/docs/notebooks/run_inference_gemma
Do sentiment analysis with a Gemma modelhttps://cloud.google.com/dataflow/docs/notebooks/gemma_2_sentiment_and_summarization
AI and ML https://cloud.google.com/docs/ai-ml
Application development https://cloud.google.com/docs/application-development
Application hosting https://cloud.google.com/docs/application-hosting
Compute https://cloud.google.com/docs/compute-area
Data analytics and pipelines https://cloud.google.com/docs/data
Databases https://cloud.google.com/docs/databases
Distributed, hybrid, and multicloud https://cloud.google.com/docs/dhm-cloud
Generative AI https://cloud.google.com/docs/generative-ai
Industry solutions https://cloud.google.com/docs/industry
Networking https://cloud.google.com/docs/networking
Observability and monitoring https://cloud.google.com/docs/observability
Security https://cloud.google.com/docs/security
Storage https://cloud.google.com/docs/storage
Access and resources management https://cloud.google.com/docs/access-resources
Costs and usage management https://cloud.google.com/docs/costs-usage
Infrastructure as code https://cloud.google.com/docs/iac
Migration https://cloud.google.com/docs/migration
SDK, languages, frameworks, and tools https://cloud.google.com/docs/devtools
Home https://docs.cloud.google.com/
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Run in Google Colabhttps://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb
View source on GitHubhttps://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb
RunInferencehttps://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.RunInference
Hugging Face Model Hubhttps://huggingface.co/models
Get started with AI/ML pipelineshttps://beam.apache.org/documentation/ml/overview/
auto classeshttps://huggingface.co/docs/transformers/model_doc/auto
google/flan-t5-smallhttps://huggingface.co/google/flan-t5-small
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