This course focuses on the essential strategies and tools for deploying, managing, and optimizing large language models (LLMs) in real-world environments. Participants will gain a deep understanding of LLM operations, including fine-tuning, scalability, and performance monitoring. It explores practical workflows, ethical considerations, and the integration of LLMs into production systems. Ideal for AI professionals and developers, the course equips learners with skills to harness the power of LLMs effectively while addressing challenges like bias, cost-efficiency, and system reliability.
Skills You'll Acquire
Generative AI Concepts
Building AI Agents
Workflow Automation with LangChain
Advanced Prompt Engineering
Hugging Face
Hugging Face
RAG
A foundational understanding of artificial intelligence and machine learning principles.
Basic programming knowledge, especially in languages commonly used for AI, such as Python.
Familiarity with deep learning concepts and frameworks like TensorFlow or PyTorch.
Experience with cloud computing and AI model deployment processes.
Understand the core principles and workflows for managing large language models (LLMs) in production environments.
Develop skills for fine-tuning and customizing LLMs for specific applications.
Learn techniques for optimizing scalability, reliability, and cost-efficiency of LLM-based systems.
Explore ethical considerations, including bias mitigation and responsible AI practices.
Gain practical experience with tools and platforms used for deploying and monitoring LLMs.
Curriculum
This Course contains 6 Modules.
Exploring the basics of Generative AI and its role in modern automation.
Designing effective prompts to optimize AI output quality.
Developing AI-driven software solutions tailored to business needs.
Harnessing Semantic Kernel for advanced query handling.
Strategies for deploying and scaling LLMs on Azure cloud.
Building RAG applications for enhanced information retrieval.
Constructing robust data pipelines to fuel machine learning.
Integrating message queues for efficient data workflows.
Utilizing cutting-edge databases: vector, graph, and key/value systems.
Leveraging AWS services for scalable AI solutions.
Deploying ML pipelines with Amazon Bedrock and other AWS tools.Hands-on labs
Cloud infrastructure essentials for machine learning innovation.
Optimizing ML and data engineering tasks using Databricks.
Deploying and running local LLMs like Llamafile and Mixtral.
Streamlining workflows with hybrid cloud and local model strategies.
Implementing open-source tools for cost-effective LLM operations.
Fine-tuning LLMs for niche applications.
Building versatile Generative AI solutions with open-source platforms.
