AI Orchestration: Designing the Prototype Architecture and Data Strategy

سرفصل های دوره

This course covers the fundamentals and practical applications of AI orchestration. It is structured to provide both theoretical knowledge and hands-on experience in integrating and managing AI models across different computing environments. Instructor Noah Gift explores architectures, strategies, and best practices for building robust AI solutions. Gain practical experience in balancing different deployment strategies, managing data flow, and optimizing system performance across various computing environments.

Note: This course was created by Pragmatic AI Labs. We are pleased to host this training in our library.

01 - Introduction

01 - Course introduction

02 - Course overview

03 - AI orchestration overview

02 - 1. Prompt Engineering Fundamentals

01 - Prompt engineering pyramid

02 - Chain of thought prompt Rust

03 - Chain of thought Rust prompt demo

03 - 2. AI Systems and Architecture

01 - Explaining chain of thought Rust prompt

02 - Caching for AI

03 - Optimizing local RAG

04 - Local vs. cloud models

04 - 3. Tools and Implementation

01 - Llamafile getting started Gemma

02 - Llamafile simple

03 - Rust hello world project structure

04 - AWS spot deploy ML

05 - Hugging Face workflow models

06 - GitHub AI models workflow

07 - Ollama local demo

08 - Technical training approaches

09 - Ollama modelfile rust debugger

10 - Effective AI engineering learning

11 - AI orchestration local workstation

12 - Using TMUX on Linux

13 - Using NVIDIA-SMI

14 - Ollama architecture

15 - Using Zenith GPU monitoring

16 - Compiling Rust candle GPU

05 - Conclusion

01 - Summary

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تولید کننده: LinkedIn Learning (Lynda)