BinaryBourbon

Production ML Workshop: From Prototype to Scale

Duration: 3 hours (with breaks)
Level: Intermediate to Advanced
Prerequisites: Basic ML knowledge, Python experience, familiarity with APIs

🎯 Workshop Objectives

By the end of this workshop, you’ll be able to:

• Transform a Jupyter notebook model into a production-ready service
• Implement caching strategies that reduce costs by 90%+
• Build error boundaries for graceful failure handling
• Monitor and optimize ML systems in production
• Make informed decisions about model selection and routing

📋 Agenda

Part 1: The Reality Check (30 min)

• Why 87% of ML projects never make it to production
• The hidden costs that kill ML products
• Case study: From $50K/month to $2K/month

Part 2: Building Production-Ready Services (45 min)

• From notebook to API: The right way
• Implementing the 3-layer architecture
• Hands-on: Deploy your first ML service

Part 3: Cost Optimization Strategies (45 min)

• Caching patterns that actually work
• Model routing and selection
• Hands-on: Implement semantic caching

Part 4: Error Handling & Resilience (45 min)

• Error boundaries for AI systems
• Graceful degradation patterns
• Hands-on: Build a fallback chain

Part 5: Monitoring & Scaling (30 min)

• Metrics that matter
• Setting up alerts
• Capacity planning for growth

🛠️ Pre-Workshop Setup

Please complete these steps before the workshop:

# Clone the workshop repository
git clone https://github.com/BinaryBourbon/production-ml-workshop
cd production-ml-workshop

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Verify setup
python verify_setup.py

Required Accounts (Free Tiers)

• OpenAI API key (we’ll use ~$1 worth of credits)
• Redis Cloud account (free tier)
• GitHub account

💻 What We’ll Build

A complete production ML system featuring:

• FastAPI service with proper error handling
• Redis-based caching layer
• Multi-model routing logic
• Monitoring dashboard
• Load testing suite

📚 Materials Included

• Complete code examples
• Architecture diagrams
• Cost calculator spreadsheet
• Monitoring dashboard template
• Post-workshop resources

👤 About the Instructor

Benjamin “BinaryBourbon” Bourbon
Senior Staff Engineer with 12+ years in production ML. Previously at Amazon and Stripe. Currently helping teams ship ML systems that actually work.

🎟️ Registration

• Next Session: February 20, 2025 @ 6 PM PST
• Format: Virtual (Zoom)
• Capacity: 30 participants
• Cost: Free

Register Here →

📧 Questions?

Email workshop@binarybourbon.dev or open an issue in the repository.

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“The best model is the one that ships.” - Let’s ship yours.