Master Large Language Models Through Real Practice
Build expertise in LLM development and deployment with hands-on projects that mirror real industry challenges. Our comprehensive program starts October 2025.
Common Roadblocks We Address
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Model Fine-Tuning Complexity
Many developers struggle with adapting pre-trained models for specific use cases without proper guidance on data preparation and hyperparameter optimization.
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Deployment Architecture Decisions
Choosing between cloud services, on-premise solutions, and hybrid approaches often leads to costly mistakes in production environments.
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Performance Optimization Bottlenecks
Understanding how to balance model accuracy with inference speed and memory usage requires practical experience with real workloads.
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Integration with Existing Systems
Connecting LLM capabilities to current business applications presents unique challenges that theoretical knowledge doesn't address.
Find Your Learning Path
Different backgrounds require different approaches. Here's how we match your experience to the right program track.
What's your current experience with machine learning?
Complete beginners start with our Foundation Track covering Python fundamentals and basic ML concepts. Those with ML experience can jump to our Advanced Track focusing specifically on transformer architectures.
Are you working in a specific industry?
Healthcare, finance, and legal professionals get specialized modules addressing domain-specific requirements like compliance, data privacy, and industry terminology processing.
What's your primary goal?
Career changers follow our comprehensive curriculum with job search support. Current developers take focused modules on integrating LLMs into existing applications. Entrepreneurs get business-focused guidance on product development.
How much time can you dedicate?
Full-time students complete the program in 16 weeks with intensive daily sessions. Part-time learners spread the same content over 9 months with weekend workshops and evening sessions.
What You'll Actually Build
Each module focuses on creating working applications that demonstrate real-world LLM capabilities.
Foundation & Architecture
- Transformer architecture deep dive
- Attention mechanisms and self-attention
- Tokenization strategies and embeddings
- Pre-training vs fine-tuning approaches
- Model size considerations and trade-offs
Development & Training
- Setting up training environments
- Data preprocessing and cleaning pipelines
- Custom dataset creation and validation
- Hyperparameter tuning strategies
- Monitoring training progress and debugging
Deployment & Production
- API design for LLM services
- Container orchestration and scaling
- Load balancing and caching strategies
- Monitoring inference performance
- Cost optimization in cloud environments
Integration & Applications
- Building chatbot interfaces
- Document analysis and summarization
- Code generation and review systems
- Multi-modal applications with vision
- RAG systems for knowledge bases
Learn from Industry Practitioners
Our instructors work at companies actively deploying LLM solutions in production environments.
Zara Kellund
Senior ML Engineer
Currently leading transformer model optimization at a major tech company. Zara has deployed models serving millions of users and specializes in making complex architectures practical for real-world applications. She brings 8 years of production ML experience to our program.
Rhea Vossen
AI Product Architect
Rhea has built LLM-powered products from concept to deployment across healthcare and finance industries. Her practical approach focuses on solving business problems with appropriate technology choices. She's particularly skilled at navigating regulatory requirements in sensitive domains.
Ready to Start Building?
We're accepting applications for our October 2025 cohort. Class size is limited to ensure personalized attention and meaningful project collaboration.