
Online Class LLM on Production - Batch 2
Temen-temen kalau dah tertarik di LLM tapi nggak belajar gimana cara kita shipping model LLM kita ke production, gass join!!
Bonus:
- Video based supplementary material (untuk membantu pembelajaran Online Class)
- Tugas besar dan dibimbing langsung saat praktik!
Kelas Dimulai : 5 Oktober, 2024
Setiap Sabtu: 20.00-22.00 WIB
Intensive 7 Pertemuan!
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What You Get in AI Online Class
- Interactive Class
- Live Question and Answer with Mentor
- e-Certificate
- Portfolio
- Interactive Class
Material
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Session 1: Introduction to LLM on Production ✨
• Overview of LLM (Large Language Model) Applications in Production
• Importance of LLM in Various Industries
• Challenges and Considerations in Deploying LLM Models
Session 2: LLM Managed Service (Claude-3, OpenAI) ✨
• Introduction to Claude-3 and Other Managed LLM Services
• Comparison of Features and Pricing
• Use Cases and Benefits of Using Managed LLM Services
Session 3: Building RAG Using Vector Database ✨
• Understanding RAG (Retrieval-Augmented Generation) Models
• Introduction to Vector Databases PgVector
• Practical Implementation of RAG Using a Vector Database
Session 4: Handling with Open Source LLM (Loading, Fine-Tuning, etc.) ✨
• Overview of Popular Open-Source LLM Frameworks (e.g., Hugging Face Transformers, LLAMA)
• Loading Pre-Trained Models and Datasets
• Techniques for Fine-Tuning LLMs for Specific Tasks
Session 5: Optimizing Your Open Source LLM Deployment ✨
• Best Practices for Deploying LLMs in Production Environments
• Performance Optimization Tips (e.g., Batch Size, Hardware Acceleration, Quantization)
Session 6: Online vs Offline Metrics for LLM Applications ✨
• Understanding Metrics for Evaluating LLM Performance in Online vs. Offline Scenarios
• Key Metrics: Accuracy, Latency, Throughput, Cost-Efficiency
Session 7: Building RAG Using Open Source LLM ✨
• Step-by-Step Guide to Building Retrieval-Augmented Generation Models Using Open-Source LLM Frameworks
• Integrating with External Data Sources for Retrieval
• Advanced Techniques and Extensions for RAG Models