What you'll learn

Master volume mapping to efficiently manage model storage, cut redundant data retrieval, optimize weight storage, and speed up access by using local storage str
Master deploying AI models with vLLM, handle thousands of requests, and design modular architectures for efficient model downloading and inference
Create a conversational AI chatbot using Python, integrating OpenAI's API for seamless, real-time chats with deployed language models
Use FastAPI and vLLM to build efficient, OpenAI-compatible APIs. Deploy REST API endpoints in containers for seamless AI model interactions with external apps
Use concurrency and synchronization for model management, ensuring high availability. Optimize GPU use to efficiently handle many parallel inference requests
Design scalable systems with efficient scaling via local model weights and storage. Secure apps using advanced authentication and token-based access control
Execute GPU or CPU intensive functions of your locally running application on a Modal powerful remote infrastructure
Deploy AI Models with a single command to run on a remote infrastructure defined in your application code
Implement Web APIs: Transform Python functions to web services using FastAPI in Modal, integrating with multi-language applications effectively

Requirements

Basic Python Skills: Familiarity with Python programming, as the course involves scripting and using Python-based tools.
Understanding of Machine Learning Concepts: A foundational grasp of machine learning principles and workflows will help in the application of deployment strategies.
Experience with Command Line Interfaces: Competence in using command line tools for installing packages and running scripts is beneficial.
Access to a Computer with Internet: A reliable computer setup with internet access is necessary to follow along with the cloud-based exercises and deployments.

Description

This course offers a blend of theoretical understanding and practical application with heavy hands-on lessons designed to transition learners from fundamentals to advanced deployment strategies. You will not only learn to deploy AI models in multiple ways, but also to build Chat Bot with Memory that will interact with our own production grade inference endpoint that will be able to support thousands of requests. Gain the expertise to deploy scalable, interactive AI applications with confidence and efficiency. Whether you're building apps for business, customer interaction, or personal projects, this course is your gateway to mastering AI model deployment. This course will equip you with the knowledge and skills to design robust inference services using cutting-edge tools such as the vLLM framework, FastAPI, and Modal.

What you will learn:

Strategic Volume Mapping for Efficient Model Management: Understand how to map and manage storage volumes meticulously to reduce redundant data retrieval and optimize model weight storage. Gain insights into leveraging local volumes for faster data access and persistent storage, minimizing unnecessary downloads from external repositories like Hugging Face.Deploying High-Performance AI Models: Master the deployment of machine learning models using the vLLM framework, supporting thousands of parallel inference requests for production-grade applications. Learn to craft a modular architecture with distinct services for model downloading and inference tasks, reflecting modern software design practices.Developing a Conversational AI Chat Application: Transform theoretical knowledge into a tangible product by developing a simple Python script to manage chat interactions with deployed language models. Integrate and authenticate using OpenAI's API client to experience seamless, real-time chat dialogue execution.Building Robust APIs with FastAPI and vLLM : Create and integrate APIs using FastAPI and vLLM to serve AI models efficiently, ensuring OpenAI-compatible interactions within a containerized infrastructure. Implement REST API endpoints for inferencing services to facilitate interactions with external applications through standardized interfaces.Efficient Resource and Model Management: Employ concurrency and synchronization techniques to manage model data between services, ensuring high availability without excessive network traffic. Optimize the use of GPUs and other hardware resources to handle a high number of parallel inference requests.Scalable and Secure Service Design: Design scalable systems that allow rapid initialization and efficient scaling through the strategic use of model weights and local storage. Secure your application using advanced authentication protocols, including token-based access control to restrict API endpoint usage to authorized users.Also this course provides an practical exploration of deploying and scaling machine learning models with only a few lines of Python decorators, using Modal's Infrastructure as a Code serverless platform and integration API's. Introduction to Modal: Begin with an

Overview

of Modal's innovative infrastructure management, which simplifies scaling and deployment by automating processes traditionally handled by platforms like AWS. Discover the benefits of serverless architecture and cost optimization strategies.Environment Setup and Script Execution: Learn how to set up and connect your local environment to Modal, manage dependencies, and execute Python scripts in both local and remote settings. Understand Modal's unique approach to deploying serverless functions and the differences between local and remote execution.Ephemeral and Deployed Applications: Transition from running ephemeral applications locally to deploying them for remote execution. Explore the lifecycle of Modal applications, lazy initialization, and container management, with a focus on cost-effective deployment strategies for high-performance workloads.Defining Infrastructure and API Integration: Dive into configuring infrastructure using Modal decorators, manage Docker-like operations, and transform Python functions into web-accessible services using Modals integrated FastAPI. Learn to navigate container management and performance considerations for optimal runtime.Advanced Deployment Techniques: Utilize classes and lifecycle hooks for efficient resource management, maintaining application state across requests, and extending container life. Gain insights into deploying machine learning models from Hugging Face and integrating large language models into your applications.Authentication and Environment Configuration: Master the process of managing secrets for authentication, configuring GPU resources, and setting up container environments. Understand the importance of keeping containers and models ready for quick inference requests.Full Deployment Workflow: Experience a complete workflow for deploying a machine learning model as a web service. From setup to ensuring service availability with cron jobs, observe best practices in container lifecycle management and DevOps automation.

Who this course is for

This course is designed for software developers, and IT professionals who are looking to elevate their skills in deploying and scaling machine learning models in a cloud environment
Those who want to move beyond traditional infrastructure challenges like manual scaling and complex server setups and are interested in leveraging serverless architecture for streamlined operations.
Learners who appreciate a hands-on approach to learning, focusing on implementing real-world solutions involving API integration, container management, and cost-effective deployment strategies.
Individuals who wish to deepen their understanding of cloud-based technologies, specifically around optimizing machine learning workflows using platforms like Modal.
Homepage:

https://www.udemy.com/course/ai-in-production-a-crash-course-in-modal-cloud-for-llms-inference/