AI in Prod: A Crash Course in Modal Cloud for LLMs Inference
AI in Prod: A Crash Course in Modal Cloud for LLMs Inference
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 35m | 2.04 GB
Instructor: Petar Petkanov
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 35m | 2.04 GB
Instructor: Petar Petkanov
From IDE to Cloud: Harness Modal's Power for Serverless Architecture, Seamless Integration and Efficient Resource Use
What you'll learn
- Deploy ML Models from your Code Editor Seamlessly: Master serverless architecture with Modal for easy scaling, infrastructure management, and cost optimization.
- Implement Web APIs: Transform Python functions to web services using FastAPI in Modal, integrating with multi-language applications effectively
- Optimize Resources and Costs: Develop strategies for efficient resource use, state persistence, and container uptime to enhance cost-effectiveness.
- Streamline ML Workflows: Configure infrastructure as code, manage secrets, and deploy AI services with Modal for efficient ML workflows.
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
Elevate your machine learning and LLM deployment skills with this basic course on the Modal Cloud Platform. Designed for application developers this course provides an practical exploration of deploying and scaling machine learning models from your code editor using Modal's serverless infrastructure 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.
By the end of this course, you'll have the skills and confidence to efficiently deploy machine learning applications on Modal's platform, leveraging its cloud-based capabilities to enhance performance and scalability. This course is ideal for anyone looking to minimize the complexity of infrastructure management and maximize the efficiency of their machine learning deployments. Join us and transform your approach to ML/LLM deployment today!
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.
AI in Prod: A Crash Course in Modal Cloud for LLMs Inference
