Deploying GPT & Large Language Models (LLMs)
Published by Pearson
Mastering the Art of Scalable and Efficient AI Model Deployment
- Learn to deploy AI applications using the latest frameworks like Kubernetes, Kubeflow, and GGUF, ensuring you're equipped with the most up-to-date skills in the industry.
- Engage with practical use cases and live coding sessions that directly translate to real-world scenarios, making the lessons immediately applicable to your work.
- Master techniques for cost management, compute optimization, and model quantization, enabling you to deploy scalable and efficient AI systems that maximize performance while minimizing resources.
This course is designed to equip software engineers, data scientists, and machine learning professionals with the skills and knowledge needed to deploy AI models effectively in production environments. As AI continues to revolutionize industries, the ability to deploy, manage, and optimize AI applications at scale is becoming increasingly crucial. This course covers the full spectrum of deployment considerations, from leveraging cutting-edge tools like Kubernetes,
Kubeflow, and GGUF, to mastering cost management, compute optimization, and model quantization.
Over the span of this course, participants dive into practical, real-world scenarios, learning how to deploy transformer models, handle model drift, and implement continuous learning systems. By the end of the course, you not only understand the technical aspects of AI deployment but also gain the strategic insights needed to balance performance, accuracy, and resource efficiency. The course is essential for anyone looking to advance their AI deployment capabilities and ensure their models are production-ready and scalable.
What you’ll learn and how you can apply it
- Deploy and manage AI models in production using Kubernetes, Kubeflow, and other state-of-the-art tools.
- Optimize model performance and resource usage through techniques like quantization and GGUF.
- Detect and address model drift to maintain the accuracy and reliability of AI applications over time.
- Implement scalable AI systems that balance cost, compute, and efficiency for real-world deployment.
This live event is for you because...
- You are a Machine Learning Engineer - Ready to refine your skills in deploying and optimizing large language models for production environments, ensuring they are both scalable and efficient.
- You are a Software Developer - Eager to enhance your expertise in deploying AI-driven applications using Kubernetes, Kubeflow, and other advanced tools.
- You are a Data Scientist - Looking to implement best practices for managing model drift and optimizing AI pipelines to maintain high accuracy and reliability.
- You are a DevOps Engineer - Focused on mastering the orchestration of AI models in production, managing resources, and automating deployment workflows for large-scale AI applications.
Prerequisites
- Intermediate Python Skills: A solid understanding of Python is essential, as it will be the primary programming language used for demonstrating AI agent integration and handling data.
- Foundational Knowledge of AI and Machine Learning Concepts: Familiarity with basic AI and machine learning principles is crucial to grasp the more advanced topics covered in the course.
- Introductory Experience with NLP Models: Having some prior experience with Natural Language Processing (NLP) models will be beneficial, as the course will delve into integrating these models with AI agents for various applications.
Course Set-up
- Python Environment: Ensure that Python is installed on your machine. We recommend using the Anaconda distribution for its ease of use and compatibility with data science libraries.
- GitHub Repository: Access course materials, including code samples and datasets, from https://github.com/sinanuozdemir/oreilly-hands-on-gpt-llm. The repository will contain all necessary files for hands-on exercises and example implementations.
- Necessary Libraries: Install the required Python libraries using pip or conda found on Github.
Recommended Preparation
- Read: Introduction to Transformers for NLP: With the Hugging Face Library and Models to Solve Problems by Shashank Mohan Jain
- Attend: Hands-on NLP by Sinan Ozdemir
- Explore: AI Unveiled Playlist by Sinan Ozdemir
Recommended Follow-up
- Read: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs, 2e, by Sinan Ozdemir:
- Watch: Quick Start Guide to Large Language Models: ChatGPT, Llama, Embeddings, Fine-Tuning and Multimodal AI by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Session 1: Fundamentals of AI Model Deployment (60 minutes)
- Overview of key considerations in deploying AI models, including cost, compute, and scalability.
- Introduction to Kubernetes (K8s) and Kubeflow for managing AI deployments.
- Best practices for deploying large language models (LLMs) like GPT in production environments.
- Q&A + Break
Session 2: Optimizing AI Models for Production (60 minutes)
- Techniques for model quantization and efficient inference using GGUF.
- Strategies for managing and reducing deployment costs.
- Hands-on exercise: Implementing model quantization and deploying a quantized model using Kubeflow.
- Q&A + Break
Session 3: Managing Model Drift and Continuous Learning (60 minutes)
- Understanding model drift and its impact on production AI systems.
- Techniques for detecting, addressing, and mitigating model drift.
- Continuous learning systems: Keeping your models updated and relevant in dynamic environments.
- Q&A + Break
Session 4: Advanced Deployment Techniques and Course Wrap-Up (50 minutes)
- Exploring advanced topics like embedding models, prompt engineering, and few-shot learning.
- Real-world case studies: Successful AI deployments at scale.
- Hands-on exercise: Deploying a production-ready AI application with real-time monitoring.
Recap of key concepts and takeaways (10 minutes)
- Final thoughts, recommendations, and next steps.
- Course feedback and evaluation.
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.