WA3309
Comprehensive Generative AI for Developers Training
This comprehensive five-day Generative AI (Gen AI) training BootCamp is tailored for developers who want to use Gen AI and large language models (LLMs) to build intelligent, scalable applications.
This Gen AI course covers a wide range of topics, from the foundations of LLMs to advanced techniques like fine-tuning of LLMs, Retrieval Augmented Generation (RAG), and Vector Embeddings. Attendees also learn how to integrate LLMs into development pipelines.
Course Details
Duration
5 days
Skills Gained
- Understand the architecture and capabilities of Large Language Models (LLMs)
- Integrate LLMs into applications using popular frameworks like LangChain, OpenAI, HuggingFace, and LlamaIndex
- Develop effective prompts to accelerate software engineering through code generation workflows and pair programming best practices
- Implement advanced techniques such as fine-tuning, RAG, and Vector Embeddings to enhance application functionality in enterprise contexts
- Apply best practices for secure, efficient, and maintainable LLM integration in software development pipelines
Prerequisites
- Practical experience in Python (at least 6 months):
- Data Structures, Functions, Control Structures
- Exception Handling, File I/O, async, concurrency (recommended)
- Practical experience with these Python libraries: Pandas, NumPy, and scikit-learn
- Understanding of Machine Learning concepts - regression, clustering, classification
- ML Algorithms: Gradient Descent, Linear Regression
- Loss Functions and evaluation metrics
Target Audience
- Programmers
- Software Engineers
- Computer Scientists
- Data Scientists
- Data Engineers
- Data Analysts
Course Outline
- LLM Foundations
- Introduction to Generative AI for Software Development
- Generative Models and their Use Cases
- Transformer architecture and its impact on LLM performance
- LLM Training Process - pre-training, fine-tuning, and reinforcement learning
- Exploring Real-World LLM Applications
- Speaking to LLMs: Prompt Engineering
- Prompt Engineering Introduction
- Techniques for creating effective prompts
- Zero-Shot Learning, Few-Shot, and Chain-of-Thought
- Prompt Engineering for Developers
- Leverage LLMs for code generation, completion, and analysis
- Best practices for prompt design and optimization in a development context
- Optimize prompting workflows for next-generation scripting
- Handle and process LLM-generated code
- Integrate prompts into development pipelines
- Accessing LLMs via APIs
- Accessing GPT 3.5 and GPT 4 via the OpenAI API
- Roles and Conversation Threading
- Popular LLMs, APIs, and Libraries - Generative AI Tech Stack
- LangChain for Integration
- Closed-Source LLMs vs Open-Source LLMs
- Chat Agents for Querying Developer Documentation via API
- Enhancing LLMs with Fine-Tuning
- State of the Art Open-Source LLMs
- Building Pipelines with HuggingFace Transformers Library
- Fine-Tuning with the Hugging Face Transformers library and code-specific data
- Building LLM-powered Applications
- Vector Embeddings
- Ingesting Private Data with LlamaIndex
- Types of Indexing and Chunking for Data Ingestion
- Introduction to Retrieval Augmented Generation (RAG)
- Semantic Search for Code libraries
- LangChain Integration and Advanced RAG
- LLM Chains and Prompt Templates
- The LangChain “Tools” Library
- Enterprise-grade RAG Pipelines
- RAG Pipeline Optimization and Performance Monitoring
- Enterprise API Applications
- Generative AI Tech Stack
- Scalable and Efficient Architectures
- Privacy/Security Considerations with Enterprise Data
- Conversational Agents in Enterprise
- Best Practices for production-ready LLM Applications
- Enterprise Application Pipelines
- Choosing the right foundation model
- Cost and ROI Evaluation Strategy
- LLM Deployment for Developers
- LLM Deployment Frameworks
- Introduction to LLMOps for Developers
- LLM Security Considerations
- Enterprise Privacy
- Cloud Deployment vs Local (Private) Serving
Upcoming Course Dates
