Приєднатися Увійти

fabricia conceição menez mota

Учасник із 2025

Діамантова ліга

Кількість балів: 9586
Gen AI: Unlock Foundational Concepts Earned черв. 24, 2026 EDT
Gen AI: Beyond the Chatbot Earned черв. 23, 2026 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned квіт. 9, 2026 EDT
Build a Data Warehouse with BigQuery Earned квіт. 7, 2026 EDT
Build a Certification Study Guide: PDE Exam Prep Earned бер. 26, 2026 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned бер. 11, 2026 EDT
[DEPRECATED] Feature Engineering Earned бер. 9, 2026 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned бер. 3, 2026 EST
Create ML Models with BigQuery ML Earned бер. 2, 2026 EST
Working with Notebooks in Vertex AI Earned лют. 26, 2026 EST
Introduction to AI and Machine Learning on Google Cloud Earned трав. 13, 2025 EDT
Build a Certification Study Guide: PMLE Earned бер. 11, 2025 EDT

Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

Докладніше

Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

Докладніше

While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

Докладніше

Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.

Докладніше

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

Докладніше

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

Докладніше

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

Докладніше

Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

Докладніше

Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

Докладніше

This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

Докладніше

This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

Докладніше

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.

Докладніше