Member since 2025
Introduction to Data Engineering on Google Cloud
Earned Sep 23, 2025 EDT
Encoder-Decoder Architecture
Earned Sep 21, 2025 EDT
Transformer Models and BERT Model
Earned Sep 21, 2025 EDT
Prompt Design in Agent Platform
Earned Sep 18, 2025 EDT
In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
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 architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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 BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.