Overview

## Become an AI Developer with Practical Skills Start your journey to becoming an AI Engineer by learning how to integrate AI into software applications. In this Track, you'll gain hands-on experience using APIs and open-source libraries to create AI-powered systems that deliver enhanced functionality and user experiences. ## Master the Tools of the Trade Explore the essential tools and technologies used by AI Engineers, including: * The OpenAI API for leveraging powerful language models like GPT * Hugging Face's extensive repository of pre-trained models and datasets * LangChain for building applications with language models, prompts, chains, and agents * Pinecone vector database for efficient similarity search and recommendation systems Through practical exercises and real-world projects, you'll learn to utilize these tools to build chatbots, recommendation engines, semantic search, and more. ## Unlock the Power of Language Models Discover the potential of Large Language Models (LLMs) and how they're revolutionizing AI application development. Learn prompt engineering techniques to optimize model outputs for your specific use cases. Explore how embeddings can be used to create more advanced AI applications like semantic search and recommendation engines. ## Build Production-Ready AI Systems Gain insights into LLMOps, the practices for developing, deploying, and maintaining AI systems in production. Learn best practices for integrating third-party APIs reliably, handling rate limits and exceptions, and structuring model outputs for robustness. Apply software engineering principles to write modular, well-documented, and testable code. ## Launch Your Career as an AI Engineer By the end of this Track, you'll have the skills and portfolio to: * Develop AI-powered applications using industry-standard tools and best practices * Integrate AI functionality into backend systems and user-facing applications * Collaborate with data scientists and software engineers to bring AI projects to life * Stay at the forefront of the rapidly evolving AI landscape

Syllabus

Working with the OpenAI API

Start your journey developing AI-powered applications with the OpenAI API. Learn about the functionality that underpins popular AI applications like ChatGPT.

Prompt Engineering with the OpenAI API

Dive deep into the principles and best practices of prompt engineering to leverage powerful language models like ChatGPT to solve real-world problems.

Planning a Trip to Paris with the OpenAI API
Working with Hugging Face

Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.

LLMOps Concepts

Learn about LLMOps from ideation to deployment, gain insights into the lifecycle and challenges, and learn how to apply these concepts to your applications.

Developing AI Systems with the OpenAI API

Leverage the OpenAI API to get your AI applications ready for production.

Organizing Medical Transcriptions with the OpenAI API
Introduction to Embeddings with the OpenAI API

Unlock more advanced AI applications, like semantic search and recommendation engines, using OpenAI's embedding model!

Topic Analysis of Clothing Reviews with Embeddings
Vector Databases for Embeddings with Pinecone

Discover how the Pinecone vector database is revolutionizing AI application development!

Software Engineering Principles in Python

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Developing LLM Applications with LangChain

Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.

Taught by

Adam Spannbauer, James Chapman, Eduardo Oliveira, Fouad Trad, Emmanuel Pire, Jonathan Bennion, Jacob Marquez, Ryan Ong, Francesca Donadoni, and Max Knobbout