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Francisco Colomer

成为会员时间：2023

钻石联赛

43048 积分
Google DeepMind: 02 Represent Your Language Data Earned Jan 9, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model Earned Jan 4, 2026 EST
Discover Business Value for Customers Earned Dec 31, 2025 EST
Partner Pre-Sales Readiness Training Earned Dec 26, 2025 EST
Modernizing Mainframe Applications with Google Cloud Earned Dec 22, 2025 EST
创建和管理 AlloyDB 实例 Earned Dec 19, 2025 EST
创建和管理 Bigtable 实例 Earned Dec 17, 2025 EST
创建和管理 Cloud Spanner 实例 Earned Dec 15, 2025 EST
创建和管理 Cloud SQL for PostgreSQL 实例 Earned Dec 11, 2025 EST
Vertex AI Studio 简介 Earned Dec 5, 2025 EST
生成式 AI 简介 Earned Dec 4, 2025 EST
Introduction to Reliable Deep Learning Earned Nov 30, 2025 EST
用　Database Migration Service　将 MySQL 数据迁移至 Cloud SQL Earned Nov 27, 2025 EST
Enterprise Database Migration Earned Nov 25, 2025 EST
Select a Google Cloud Database for Your Applications Earned Sep 24, 2025 EDT
Google Cloud 基础知识：核心基础设施 Earned Jul 26, 2025 EDT
Machine Learning in the Enterprise Earned Sep 29, 2024 EDT
How Google Does Machine Learning Earned May 29, 2024 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Nov 15, 2023 EST
使用 BigQuery 构建数据仓库 Earned Nov 14, 2023 EST
在 Google Cloud 上为机器学习 API 准备数据 Earned Nov 13, 2023 EST
Serverless Data Processing with Dataflow: Operations Earned Nov 9, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Nov 4, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Oct 26, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Oct 23, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Oct 22, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Oct 20, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Oct 14, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Oct 12, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Oct 4, 2023 EDT

In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.

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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.

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The course aims to train Google technical sales partners on the business value discovery process using proprietary content. Course activities use an external tool (Yoodli). Refer to Yoodli's Terms of Service and Privacy Notice.

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Want to learn more about Google Cloud? Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.

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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.

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完成入门级技能徽章课程创建和管理 AlloyDB 实例，展示您在以下方面的技能：执行核心 AlloyDB 操作 和任务、从 PostgreSQL 迁移到 AlloyDB、管理 AlloyDB 数据库，以及 使用 AlloyDB 列式引擎加速分析查询。

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完成入门级技能徽章课程创建和管理 Bigtable 实例，展示以下方面的技能：创建实例、设计架构、 查询数据，以及在 Bigtable 中执行管理任务，包括监控性能、配置节点自动扩缩和复制。

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完成创建和管理 Cloud Spanner 实例 这一入门级技能徽章课程，展示您在以下方面的技能： 创建 Cloud Spanner 实例和数据库并与之互动； 使用各种方法加载 Cloud Spanner 数据库； 备份 Cloud Spanner 数据库；定义架构并了解查询计划； 部署连接到 Cloud Spanner 实例的现代 Web 应用。

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完成“创建和管理 Cloud SQL for PostgreSQL 实例”这一入门级的技能徽章课程，展示您在以下方面的技能： 迁移、配置和管理 Cloud SQL for PostgreSQL 实例及数据库。

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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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This course introduces you to the world of reliable deep learning, a critical discipline focused on developing machine learning models that not only make accurate predictions but also understand and communicate their own uncertainty. You'll learn how to create AI systems that are trustworthy, robust, and adaptable, particularly in high-stakes scenarios where errors can have significant consequences.

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完成用　Database Migration Service　将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程，展示您在以下方面的技能： 使用 Database Migration Service 中提供的不同作业类型和连接选项，将 MySQL 数据迁移到 Cloud SQL； 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。

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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.

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In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud. You explore relational and NoSQL databases, dive into Cloud SQL, AlloyDB, and Spanner, and learn how to align database strengths with your application requirements, including those of generative AI. Gain hands-on experience configuring Vector Search and migrating applications to the cloud.

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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。

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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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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.

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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