Hugging Face Transformers Training

Master the World’s Leading Open-Source AI Platform

Transform Your Team into AI Practitioners in 3 Days

Learn to leverage thousands of pre-trained models and build production-ready AI solutions with the Hugging Face ecosystem. Our hands-on training combines deep technical knowledge with real-world deployment strategies.

🎯 Course Overview

This intensive 3-day course takes you from Hugging Face fundamentals to advanced production deployment. You’ll work with real models, real data, and real deployment scenarios.

What You’ll Master

• 🤖 Model Selection: Choose the right model from 500,000+ options
• 🔧 Fine-Tuning: Adapt models to your specific use cases
• 🚀 Deployment: Scale from prototype to production
• 💰 Optimization: Reduce costs while improving performance
• 🔒 Security: Implement enterprise-grade safety measures

Who Should Attend

• Data Scientists transitioning to LLMs
• ML Engineers building AI applications
• DevOps teams deploying AI models
• Technical leaders evaluating AI platforms
• Developers integrating AI capabilities

📚 Detailed Curriculum

Day 1: Foundations & Core Concepts

Morning Session: Hugging Face Ecosystem

• Introduction to Transformers

  • Architecture deep dive
  • Attention mechanisms explained
  • Model categories: BERT, GPT, T5, and beyond

• Hugging Face Hub

  • Navigating the model zoo
  • Understanding model cards
  • Licensing and usage rights
  • Community best practices

• Hands-On Lab 1: First Model Deployment

  • Load and run your first model
  • Basic inference pipeline
  • Performance benchmarking

Afternoon Session: Working with Pre-trained Models

• Model Selection Strategies

  • Matching models to use cases
  • Performance vs. accuracy trade-offs
  • Multilingual considerations

• Tokenization Deep Dive

  • Tokenizer types and strategies
  • Handling special tokens
  • Custom vocabulary extension

• Hands-On Lab 2: Multi-Model Comparison

  • Compare 5 different models
  • Benchmark speed and accuracy
  • Create selection criteria

Day 2: Fine-Tuning & Customization

Morning Session: Fine-Tuning Fundamentals

• Data Preparation

  • Dataset formats and loaders
  • Data quality assessment
  • Augmentation strategies

• Training Strategies

  • Full fine-tuning vs. PEFT
  • Learning rate scheduling
  • Gradient accumulation
  • Mixed precision training

• Hands-On Lab 3: Fine-Tune Your First Model

  • Prepare custom dataset
  • Configure training parameters
  • Monitor training progress
  • Evaluate results

Afternoon Session: Advanced Fine-Tuning

• Parameter-Efficient Fine-Tuning (PEFT)

  • LoRA implementation
  • QLoRA for large models
  • Adapter methods

• Multi-Task Learning

  • Shared representations
  • Task-specific heads
  • Loss balancing

• Hands-On Lab 4: Production Fine-Tuning

  • Implement LoRA on large model
  • Multi-GPU training setup
  • Checkpoint management
  • A/B testing preparation

Day 3: Production Deployment & Optimization

Morning Session: Deployment Strategies

• Model Optimization

  • Quantization techniques
  • Pruning strategies
  • Knowledge distillation
  • ONNX conversion

• Serving Infrastructure

  • Hugging Face Inference Endpoints
  • Self-hosted deployment options
  • Container strategies
  • Serverless considerations

• Hands-On Lab 5: Deploy to Production

  • Optimize model for inference
  • Create Docker container
  • Deploy to cloud platform
  • Set up monitoring

Afternoon Session: Scaling & Operations

• Performance Optimization

  • Batch processing strategies
  • Caching mechanisms
  • Load balancing
  • Auto-scaling policies

• Monitoring & Maintenance

  • Performance metrics
  • Model drift detection
  • A/B testing frameworks
  • Update strategies

• Hands-On Lab 6: Enterprise Integration

  • Build REST API wrapper
  • Implement rate limiting
  • Add authentication
  • Create monitoring dashboard

🛠️ Hands-On Projects

Throughout the course, you’ll build:

1. Text Classification System

   • Fine-tune BERT for domain-specific classification
   • Deploy with <1 second response time
   • Handle 1000+ requests per second

2. Question-Answering Engine

   • Implement retrieval-augmented generation
   • Optimize for accuracy and speed
   • Add multilingual support

3. Custom Language Model

   • Fine-tune GPT-style model on your data
   • Implement safety filters
   • Deploy with streaming support

💡 What Makes Our Training Different

Real Production Experience

Our instructors have deployed Hugging Face models handling millions of daily requests. You’ll learn from actual production scenarios, not just tutorials.

Custom Scenarios

We adapt examples to your industry and use cases. Bring your own data and leave with working prototypes.

Ongoing Support

• 30 days of post-training support
• Access to our private Slack community
• Monthly office hours with instructors
• Updated materials as new features release

📋 Prerequisites

Required Knowledge

• Python programming (intermediate level)
• Basic machine learning concepts
• Familiarity with NumPy/Pandas
• Command line proficiency

Technical Requirements

• Laptop with 16GB+ RAM
• Python 3.8+ installed
• Docker Desktop (we’ll help with setup)
• Cloud account (AWS/GCP/Azure) for deployment labs

💰 Pricing & Logistics

Training Options

On-Site Training

• Price: $15,000 for up to 12 participants
• Duration: 3 consecutive days
• Includes: All materials, labs, and 30-day support
• Travel: Additional cost for instructor travel

Virtual Training

• Price: $10,000 for up to 12 participants
• Duration: 3 days (6 hours per day)
• Platform: Zoom with breakout rooms for labs
• Includes: All materials, labs, and 30-day support

Public Classes

• Price: $1,995 per participant
• Schedule: Monthly in major cities
• Class Size: Maximum 20 participants
• Next Dates: View Schedule

What’s Included

• Comprehensive course materials
• Access to GPU compute for labs
• Hugging Face Pro account (3 months)
• Certificate of completion
• Post-training support package

🎓 Meet Your Instructors

Our instructors are practicing AI engineers who work with Hugging Face models daily:

• 10+ years in machine learning
• Published research in NLP/transformers
• Production deployments at scale
• Active contributors to Hugging Face ecosystem

📈 Learning Outcomes

By the end of this training, you will be able to:

✅ Select optimal models for any NLP task
✅ Fine-tune models with your own data
✅ Deploy models that scale to millions of users
✅ Optimize for cost and performance
✅ Implement proper monitoring and maintenance
✅ Build production-ready AI applications

🚀 Next Steps

Ready to Transform Your AI Capabilities?

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📚 Additional Resources

Free Resources

• Hugging Face Best Practices Guide
• Model Selection Cheat Sheet
• Fine-Tuning Cost Calculator

Related Training

• LangChain Development Course
• Vector Database Mastery
• Production AI Operations

❓ Frequently Asked Questions

Q: Do I need deep learning experience?
A: No, we cover the necessary concepts. Basic ML understanding helps but isn’t required.

Q: Can we use our own data?
A: Absolutely! We encourage it. We’ll help you prepare it for the labs.

Q: What if I can’t attend all days?
A: We offer recorded sessions for virtual training. On-site sessions can be split.

Q: Is this course updated for the latest models?
A: Yes, we update content monthly to include new models and techniques.

View More FAQs →