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Natural Language Processing (NLP) with Python and SpaCy

Course Information

  • Sessions 1 day
  • Duration 7.5 hrs
  • Level Intermediate
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Pydantic for Structured Data Validation in LLM Workflows

Course Code: C662

1 Review(s) | Add Your Review

What's This Course About

Master the art of building reliable, production-ready LLM applications with Pydantic for structured data validation. This hands-on course teaches you how to move beyond free-form AI text outputs and implement robust validation schemas that ensure every response from large language models is accurate, complete, and ready for downstream processing. Learn to design structured output schemas, parse and validate JSON responses from LLMs, and handle validation errors gracefully — skills essential for anyone building AI-powered systems that need to perform consistently at scale.

Dive deep into advanced validation patterns including nested models, enums, unions, and custom validators that give you fine-grained control over LLM outputs. Explore real-world applications such as building a customer support assistant that extracts ticket details, validates customer data, and routes requests using structured output. You will also learn to integrate Pydantic with modern LLM APIs, agent frameworks, function calling, tool calling, and backend services like FastAPI — equipping you with the expertise to design multi-step LLM pipelines where every stage is structured, validated, and production-ready.

Funding Options

No funding is available for this course

For WSQ funding, please checkout the details at NICF – Natural Language Processing (NLP) with Python for Beginners

Course Fee

$350.00 (GST-exclusive)
$381.50 (GST-inclusive)

Course Date

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1: Foundations of Structured Output & Pydantic Basics

Why free-form LLM text is not enough for production systems

What structured output is and why it matters in LLM workflows

Common failure modes in unstructured LLM responses

Introduction to Pydantic

Parsing and validating JSON responses from LLMs

Handling validation errors gracefully

Topic 2: Using Pydantic with LLM APIs & Agent Frameworks

Designing structured schemas for LLM outputs

Prompting LLMs for structured responses

Using Pydantic models directly in API calls

Structured outputs with modern LLM providers

Function calling and tool calling with Pydantic models

Ensuring completeness and correctness before triggering downstream systems

Example: Customer Support Assistant

Extracting ticket details

Validating customer data

Routing requests based on structured output

Topic 3: Advanced Validation Patterns & Production Workflows

Nested models and complex schemas

Enums, unions, and custom validators

Combining structured outputs and tool-calling in agent workflows

Using Pydantic in multi-step LLM pipelines

Defensive programming: validating at every stage

Integrating Pydantic into FastAPI or backend services

How popular frameworks use Pydantic under the hood

Designing robust LLM systems where every step is structured and validated

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • AI Engineer
  • Machine Learning Engineer
  • LLM Application Developer
  • Python Developer
  • Backend Engineer
  • Data Engineer
  • NLP Engineer
  • AI Solutions Architect
  • DevOps Engineer (AI/ML)
  • Full Stack Developer
  • Data Scientist
  • AI Product Manager
  • Conversational AI Developer
  • Automation Engineer
  • Software Engineer
  • API Developer
  • AI Research Engineer
  • MLOps Engineer
  • Technical Lead (AI Systems)
  • AI Integration Specialist

Trainers

Trainers

Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners. Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis. Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore. His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.

Review

Customer Reviews (1)

will recommend Review by Course Participant/Trainee
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The course introduces many interesting concepts which are useful for application. (Posted on 10/24/2022)

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