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Apply your skills in Google Cloud console

Rafael Alfredo Fonseca Gonzalez

Date d'abonnement : 2022

Manage Kubernetes in Google Cloud Earned déc. 2, 2025 EST
Implementing Cloud Load Balancing for Compute Engine Earned nov. 28, 2025 EST
Preparing for your Professional Cloud Architect Journey Earned nov. 25, 2025 EST
Google Cloud Fundamentals: Core Infrastructure Earned mai 22, 2025 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud Earned mai 16, 2025 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned sept. 12, 2024 EDT
Recommendation Systems on Google Cloud Earned sept. 9, 2024 EDT
Prepare Data for ML APIs on Google Cloud Earned août 23, 2024 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI Earned août 19, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned août 17, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned juil. 7, 2024 EDT
Level 3: GenAIus Travels Earned juin 25, 2024 EDT

Complete the intermediate Manage Kubernetes in Google Cloud skill badge course to demonstrate skills in the following: managing deployments with kubectl, monitoring and debugging applications on Google Kubernetes Engine (GKE), and continuous delivery techniques.

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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.

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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in. By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills. The courses should be completed in the following order: 1. Google Cloud Computing Foundations: Cloud Computing Fundamentals 2. Google Cloud Computing Foundations: Infrastructure in Google Cloud 3. Google Cloud Computing Foundations: Networking and Security in Google Cloud 4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud

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

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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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.

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Excited to follow your favorite soccer/football stars on their next quest? Use GenAIus Travel Guides to learn how to interact with chat applications, master prompt engineering, understand the importance of context in AI, and work with Generative AI. Earn an exclusive Google Cloud Generative AI Credential and showcase your new skills! No prior experience needed!

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