AI
Generative AI for Developers
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 and 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.
Objectives
- Understand Large Language Models (LLMs) and their foundational concepts, including generative AI and transformer architecture.
- Master prompt engineering techniques to effectively communicate with LLMs and achieve desired outcomes.
- Leverage LLMs to enhance software development processes, including code generation, completion, and analysis.
- Access and integrate LLMs through APIs into existing applications and services, utilizing popular libraries and frameworks like LangChain and Hugging Face Transformers.
- Build and deploy powerful, production-ready LLM-powered applications with a focus on scalability, security, and privacy.
Duration
5 days
Audience
Programmers, Software Engineers, Computer Scientists, Data Scientists, Data Engineers, and Data Analysts.
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
- 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
- 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
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.