Generative AI & Large Language Models

This course provides a comprehensive high-level overview of Generative AI, beginning with foundational concepts and terminology and then delving into specific applications such as Large Language Models (LLMs) for text generation and diffusion-based models image creation. Key lessons include an in-depth look at LLMs, AI image generation methods, and hands-on experience with tools like DALL-E and Midjourney. The course concludes by addressing practical aspects of deploying Generative AI in production environments, focusing on data collection, prompt execution, maintenance, and orchestration strategies.

Employ the abilities of Generative AI with a deep dive into fundamentals. This course examines how various models are developed, how they work, and how to use them to their full potential.

Ready to build production-grade AI? This program equips developers to deploy reliable generative AI solutions. We'll move past theory and focus on the proven implementation patterns you need. You'll master production essentials like model selection, cost estimation, and reliable prompt engineering to build efficient apps. You'll also implement lightweight model adaptation using PEFT. Then, you'll build end-to-end RAG systems, using vector databases to connect LLMs to your data and evaluate quality with frameworks like RAGAs. Finally, you'll dive into advanced multimodal applications that process text, images, and audio. You'll enforce structured outputs with Pydantic and implement system observability to build, trace, and debug modern AI apps.

This course is for product managers who are building products that contain GenAI features. It starts with teaching some underlying principles about LLMs and conversational AI, then looks at specific ways GenAI can be used in different product features, and what metrics can be used to measure the performance of GenAI features (including information retrieval accuracy and fairness/bias metrics). The course concludes with a project in which you’ll draft a proposal for a GenAI-powered product.

Bridge the gap between technology and business strategy. Explore how to evaluate generative AI’s potential, mitigate risks, and implement scalable, real-world solutions that drive sustainable growth.

Understand and apply LLMOps principles to large language models. Master the ability to fine-tune, deploy, and monitor applications while implementing AI safety measures across multiple production projects.

Build a strong foundation in generative AI with Google Cloud. This free course covers model types and shows you how to create AI-powered tools to bring your ideas to life.

Develop, evaluate, and deploy custom copilots and applications using Azure AI Studio, Prompt Flow and OpenAI large language models. You'll operationalize AI-driven applications on Azure using GenAIOps, focusing on evaluation, monitoring and continuous integration and deployment pipelines. You'll create prompt flows, integrate data sources, and manage the full lifecycle of generative AI solutions in real-world scenarios.

Master Large Language Models (LLMs) and build sophisticated text generation applications in this hands-on course. You’ll master prompt engineering techniques, optimize model selection and costs, and dive deep into Retrieval-Augmented Generation (RAG), using vector databases to ground AI responses in external data and eliminate hallucinations. Finally, you’ll evaluate system performance with RAGAS and showcase your skills by building an end-to-end RAG application.

Dive into large language models and explore real-world use cases, practical applications, and advanced techniques in this course taught by the expert Google Cloud Training team.

Explore the core of modern NLP with this free beginner course. Dive into Transformer architecture and BERT to learn more about self-attention, text classification, and question and answering.

Master the fundamentals of generative AI and develop a deep understanding of its historical context and real-world applications. Explore AI and machine learning foundations using AWS tools, large language models (LLMs) with transformer-based architectures, and the societal impacts of generative AI to understand AI's evolution and ethical deployment. Engage in exercises on AI history, machine learning approaches, prompt engineering, and responsible AI use to apply your knowledge in practical scenarios. Develop skills in distinguishing AI from ML, exploring LLMs, and emphasizing ethical AI deployment, enabling you to apply these concepts in real-world applications. Designed for learners with basic Python knowledge, this program empowers you to advance your understanding of generative AI.

Build unique AI solutions using Vertex AI Studio. This course is perfect for developers ready to explore the artistic side of tech.

This lesson covers vector databases, crucial for enhancing AI's long-term memory. Topics include core concepts, retrieval methods, and advanced indexing.

Build and deploy advanced generative AI solutions on Azure with OpenAI models, GPT Vision, and DALL-E. Create RAG pipelines, craft prompts, automate workflows, and integrate multimodal AI applications.

Learners will discover the core concepts of Generative AI and how it works across a variety of fields. This course breaks down the often complex technology into digestible content, emphasizing real-world examples and non-technical applications. Learners will also explore the ethical considerations of using AI and gain insight into how they might use generative AI in their own work and lives.