AI Engineering

Upon completion this course, learners will obtain the fundamental concepts and practical workings of OpenAI's Assistant APIs, enabling them to build intelligent, conversational agents faster than ever previously possible. Learners will begin by understanding how the API works by getting hands-on experience in creating an Assistant, initiating Threads, crafting Messages, and Running the assistant in a live environment. The final lessons encourage learners to bring together all the learned components in a capstone project, reinforcing their understanding and showcasing their new-found capabilities. This course is perfect for developers, tech enthusiasts, and AI practitioners aspiring to master the intricacies of OpenAI's cutting-edge technologies.

Learn the basics of LangChain.js and build an internal company knowledge base using retrieval from a vector database. This hands-on course takes you step by step through the process of building AI-powered applications using LangChain. You’ll start with an expert-led introduction by LangChain’s lead maintainer, Jacob Lee, giving you a strong foundation straight from the source. As you progress, you’ll: - Build app flow diagrams - Set up databases with Supabase - Refine text processing techniques - Craft powerful prompt templates - Develop starter code - Integrate logic chains for data retrieval and processing - Design a functional user interface—bringing your AI project to life Each module is designed to sharpen your coding skills with interactive challenges that push you to apply what you’ve learned in real-world scenarios. The course wraps up with an exciting solo project: building your own personal assistant app, solidifying your expertise in AI app development. With its deep focus on LangChain, this course gives you hands-on experience with cutting-edge AI tools, preparing you to build the next generation of intelligent applications.

Upon completing this course, learners will understand the distinctions between open-source and closed-source frameworks and their impact on development. The course offers hands-on experience with HuggingFace.js, enabling learners to perform inference tasks and apply AI solutions in real scenarios. Participants will gain the skills to implement text-to-speech and image transformations using HuggingFace's libraries. The focus on the HuggingFace.js interface provides direct access to leading open-source AI tools, standing out in its practical approach. A key section is on navigating the HuggingFace.js Hub, where learners discover how to access free models, essential for cost-effective AI deployment. The course culminates with insights into AI’s future in browser-based applications, preparing learners for the next wave of AI integration. What distinguishes this course is its blend of theoretical knowledge and practical application, equipping learners to effectively utilize AI models in their projects. It's ideal for those aiming to pioneer in open-source AI application development.

This course delves into the intricate process of ReAct prompting, a critical component in the design of AI agents. Through dedicated modules, learners will explore the nuanced approach to crafting prompts that effectively guide AI responses in a reactive manner. This technique is essential for developing AI agents that can interact dynamically with users and environments. The curriculum offers an in-depth examination of ReAct prompting, teaching students how to plan and construct prompts that lead to desired actions and responses from the AI. Participants will learn the principles of prompt engineering, which combines elements of psychology, linguistics, and data science to shape the AI's interactions. In addition to theoretical knowledge, the course emphasizes practical experience. Students will engage in hands-on exercises to refine their skills in building ReAct prompts, understanding the "loop" mechanism that enables continuous and context-aware interactions, and setting up code that facilitates these sophisticated dialogues. By focusing on ReAct prompting, the course prepares learners to create AI agents that are not only responsive but also proactive in their engagements, resulting in more natural and effective human-AI interactions. This specialized training is particularly beneficial for those interested in enhancing user experience and creating more intuitive AI systems.

Upon completing the this course, learners will have established a robust foundation in AI principles and hands-on proficiency with AI tools and APIs. They will learn the art of crafting and manipulating AI models, enabling them to integrate AI into applications with confidence. This course stands out by offering a detailed exploration of AI engineering, from the basics to deeper techniques like the "Few Shot" approach and the creation of images with the latest DALL·E 3 API. Participants will also learn to navigate AI safety and ethical considerations, ensuring responsible use of AI technology. With a blend of theoretical knowledge and practical application, this course is uniquely positioned to empower learners to become innovative problem solvers in the AI domain.

In this course, you will explore advanced AI engineering concepts, focusing on the creation, use, and management of embeddings in vector databases, as well as their role in Retrieval-Augmented Generation (RAG). You will start by learning what embeddings are and how they help AI interpret and retrieve information. Through hands-on exercises, you will set up environment variables, create embeddings, and integrate them into vector databases using tools like Supabase. As you progress, you will take on challenges that involve pairing text with embeddings, managing semantic searches, and using similarity searches to query data. You will also apply RAG techniques to enhance AI models, dynamically retrieving relevant information to improve chatbot responses. By implementing these strategies, you will develop more accurate, context-aware conversational AI systems. This course balances both the theory behind AI embeddings and RAG with practical, real-world applications. By the end, you will have built a proof of concept for an AI chatbot using RAG, preparing you for more advanced AI engineering tasks.

Dive into deploying secure and robust applications with our Cloudflare course. Learn to manage API requests with Cloudflare workers, boost app strength with an AI Gateway, and launch websites using Cloudflare Pages. This hands-on course also teaches you to refine error handling for smoother user experiences. Build practical skills in using Cloudflare’s features to secure APIs, enhance sites with AI, and ensure reliability. By the end of the course, you'll be skilled at using Cloudflare to boost your applications' security and performance, ready to solve real-world problems.

This course teaches you how to generate and manipulate high-quality images with Open AI's Dall-e text-to-image model. You'll then discover how to get the most out of the model using the Open AI API. Finally, you’ll integrate GPT-4 with Vision into your AI-powered apps to carry out comprehensive image analysis, including object detection, to answer questions about an image you upload, for example! Why use AI to generate images? First, it's efficient. AI can save you time and resources compared to traditional methods. Second, AI allows you to create unique images that haven't been seen before, ensuring that your work is original and stands out. Finally, it allows for creativity without using real people, enabling you to depict diverse, imaginary individuals in your visuals. By the end of this course, you'll have gotten to grips with perfecting your image generation prompts, generating images in different formats and styles, editing images, and more! Moreover, you’ll have a solid understanding of AI multimodality - systems that can process input from and produce outputs across different data formats, including text, images, audio, and video. Ready to take the next step in AI? Let's go!