What is AutoML Natural Language?
AutoML Natural Language enables training custom NLP models without machine learning expertise. The service automates model architecture selection, hyperparameter tuning and training. You provide labeled texts, Google creates an optimized model for your specific task.
Core Features
- Text Classification: Categorize documents into custom classes
- Entity Extraction: Detect and extract custom entities from text
- Sentiment Analysis: Analyze sentiment in texts at document or entity level
- AutoML Training: Automatic model optimization without manual configuration
- One-Click Deployment: Deploy trained models directly as an API
Typical Use Cases
Support Ticket Classification
Incoming support requests are automatically categorized and prioritized. The model learns from historical ticket data and assigned categorization.
Contract Analysis
Entity extraction identifies contract parties, dates, amounts and clauses in legal documents. Legal professionals can focus on exceptions rather than manual review.
Social Media Monitoring
Sentiment analysis evaluates brand mentions in social media. The model recognizes industry-specific vocabulary better than generic solutions.
Benefits
- Production-ready models in hours instead of months
- No ML infrastructure or expertise required
- Automatic scaling of inference capacity
- Continuous improvement through retraining
Integration with innFactory
As a Google Cloud Partner, innFactory supports you with AutoML Natural Language: data preparation, labeling strategy, model training and integration into existing business processes. We help evaluate whether AutoML or custom training is the better fit.
Available Tiers & Options
AutoML
- No ML knowledge required
- Automatic hyperparameter tuning
- Fast training
- Less control than custom training
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What is AutoML Natural Language?
AutoML Natural Language enables training of custom NLP models without ML expertise. The service automates model architecture, hyperparameter tuning and training based on your data.
How much training data is needed?
For text classification, at least 1,000 documents per category are recommended. For entity extraction, at least 200 examples per entity type should be provided.
How does AutoML differ from the Natural Language API?
The Natural Language API offers pre-trained models for general NLP tasks. AutoML Natural Language trains custom models on your specific data for better results on domain-specific tasks.
Is AutoML Natural Language part of Vertex AI?
Yes, AutoML Natural Language is integrated into Vertex AI. Models are trained and deployed in Vertex AI, enabling unified MLOps.
