Google Cloud Skills Boost
This Google Cloud-managed learning path, "Advanced: Generative AI for Developers," is designed for app developers, machine learning engineers, and data scientists. It builds upon the "Introduction to Generative AI" path and delves into technical aspects of generative AI. The path covers a range of topics, starting with diffusion models for image generation and exploring the attention mechanism in neural networks. It then dives into the encoder-decoder architecture used for tasks like machine translation and text summarization, before introducing Transformer models and the BERT model.
The path then moves into practical applications, teaching how to create image captioning models using deep learning. It introduces Vertex AI Studio for prototyping and customizing generative AI models and explores vector search and embeddings for building search applications with LLMs. The path concludes with modules on responsible AI, including fairness and bias mitigation, interpretability and transparency, and MLOps for generative AI, highlighting the challenges and tools for deploying and managing these models.
Advanced: Generative AI for Developers Learning Path
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A Generative AI Learning Path with a technical focus, built for App Developers, Machine Learning Engineers, and Data Scientists. Recommended prerequisite: Introduction to Generative AI learning path.
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01
Introduction to Image Generation
This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research...
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02
Attention Mechanism
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the...
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03
Encoder-Decoder Architecture
This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder...
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04
Transformer Models and BERT Model
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the...
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05
Create Image Captioning Models
This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model....
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06
Introduction to Vertex AI Studio
This course introduces Vertex AI Studio, a tool for prototyping and customizing generative AI models. Through immersive lessons, engaging demos, and a hands-on lab, you'll explore the generative AI workflow and learn how to leverage Vertex AI Studio for Gemini...
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07
Vector Search and Embeddings
This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical...
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08
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond...
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09
Responsible AI for Developers: Fairness & Bias
This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud...
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10
Responsible AI for Developers: Interpretability & Transparency
This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
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11
Machine Learning Operations (MLOps) for Generative AI
This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps...
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