Participante desde 2023
Criar embeddings, pesquisa vetorial e RAG com o BigQuery
Earned Feb 19, 2025 EST
BigQuery Fundamentals for Redshift Professionals
Earned Jan 12, 2024 EST
Redshift to BigQuery
Earned Jan 8, 2024 EST
API Design and Fundamentals of Google Cloud's Apigee API Platform
Earned Dec 18, 2023 EST
Analyzing and Visualizing Data in Looker
Earned Dec 4, 2023 EST
Este curso estuda uma solução de geração aumentada de recuperação (RAG) no BigQuery para mitigar as alucinações da IA. Ele introduz um fluxo de trabalho de RAG que engloba a criação de embeddings, a pesquisa por um espaço vetorial e a geração de respostas aprimoradas. O curso explica os motivos conceituais dessas etapas e a implementação prática delas com o BigQuery. Até o fim do curso, será possível criar um pipeline de RAG usando o BigQuery e modelos de IA generativa como o Gemini, além de modelos de embeddings, para lidar com os próprios casos de uso de alucinação de IA.
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.
This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks for migrating data from AWS Redshift to BigQuery using BigQuery Data Transfer Service, which includes sample mock data. Learners will complete a challenge lab that focuses on the process of transferring both schema and data from a Redshift data warehouse to BigQuery.
In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API life cycle, and how the Apigee API platform helps you manage all aspects of the life cycle. You learn about how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with Google Cloud's Apigee API Platform series. After completing this course, enroll in the API Security on Google Cloud's Apigee API Platform course.
In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.