The triangulation of ethical leader signals using qualitative, experimental, and data science methods

2022-12-05 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, quick

Summary

This content outlines a comprehensive end-to-end project workflow utilizing spaCy, a prominent open-source Python library for natural language processing, and Prodigy, a flexible user interface tool built on spaCy. The described workflow encompasses critical stages from initial setup to final deployment and testing. Key steps include robust package versioning, thorough data pre-processing, efficient data ingestion into a database, and interactive annotation sessions facilitated by Prodigy's user interface. Following data preparation, the workflow details model training and evaluation, culminating in Python packaging for distribution and the development of a visual application specifically designed for testing the trained model.

Key takeaway

For NLP Engineers or MLOps Engineers building production-ready systems, adopting a structured spaCy end-to-end workflow is crucial for efficiency and maintainability. This approach, integrating tools like Prodigy for annotation and covering steps from data ingestion to visual testing, ensures robust model development and deployment. You should standardize your project lifecycle to include explicit package versioning and a dedicated visual application for model validation, streamlining future updates and quality assurance.

Key insights

A complete spaCy-based NLP project workflow integrates data, annotation, training, and deployment.

Principles

• Integrate annotation with training.
• Version control all project components.
• Use a visual app for model testing.

Method

The workflow involves package versioning, data pre-processing, database ingestion, Prodigy-based annotation, model training/evaluation, Python packaging, and a visual testing application.

In practice

• Use Prodigy for efficient data annotation.
• Package models for easy deployment.
• Implement a visual testing interface.

Topics

• spaCy
• Natural Language Processing
• Prodigy
• MLOps Workflow
• Model Training
• Data Annotation
• Python Packaging

Best for: NLP Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Building new NLP solutions with spaCy and Prodigy — NLP project success hinges on iterative design, data annotation, and model integration, not just model architecture.
• Mastering spaCy — Mastering spaCy enables building structured, end-to-end NLP solutions using custom components and LLM-powered models.
• Getting My Hands Dirty with Python and NLP — Building a practical NLP project is an effective way to learn Python libraries and data structures.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.