Muhammad Herwindra Berlian
Membro dal giorno 2020
Campionato Bronzo
16660 punti
Securing your Network with Cloud Armor
Earned mag 24, 2023 EDT
Cloud Architecture: Design, Implement, and Manage
Earned mag 24, 2023 EDT
Ottimizza i costi per Google Kubernetes Engine
Earned mag 23, 2023 EDT
Create ML Models with BigQuery ML
Earned mag 20, 2023 EDT
BigQuery for Machine Learning
Earned mag 20, 2023 EDT
Creazione di una rete Google Cloud sicura
Earned mag 19, 2023 EDT
Creating Infrastructure on Google Cloud with Terraform
Earned mag 15, 2023 EDT
Google Kubernetes Engine Best Practices: Security
Earned mag 15, 2023 EDT
Crea un sito web su Google Cloud
Earned mag 14, 2023 EDT
Develop Serverless Apps with Firebase
Earned mag 13, 2023 EDT
Implementazione di Cloud Load Balancing per Compute Engine
Earned mag 12, 2023 EDT
Implement DevOps Workflows in Google Cloud
Earned mag 12, 2023 EDT
Prepara i dati per le API ML su Google Cloud
Earned mag 11, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned mag 11, 2023 EDT
Deploy Kubernetes Applications on Google Cloud
Earned mag 11, 2023 EDT
Configura un ambiente di sviluppo di app su Google Cloud
Earned mag 10, 2023 EDT
Crea l'infrastruttura con Terraform su Google Cloud
Earned mag 10, 2023 EDT
Sviluppa la tua rete Google Cloud
Earned mag 10, 2023 EDT
Set Up a Google Cloud Network
Earned set 20, 2020 EDT
Genera insight dai dati BigQuery
Earned set 20, 2020 EDT
Machine Learning APIs
Earned mag 22, 2020 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned mag 20, 2020 EDT
Deprecated Kubernetes Solutions
Earned mag 8, 2020 EDT
DEPRECATED Application Development - Java
Earned mag 8, 2020 EDT
Cloud Development
Earned mag 8, 2020 EDT
DEPRECATED Application Development - Python
Earned mag 8, 2020 EDT
DEPRECATED Cloud Architecture
Earned mag 8, 2020 EDT
Anthos: Service Mesh
Earned mag 3, 2020 EDT
[Deprecated] Data Science on Google Cloud: Machine Learning
Earned mag 3, 2020 EDT
Intro to ML: Image Processing
Earned mag 3, 2020 EDT
Google Developer Essentials
Earned mag 3, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned mag 3, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned mag 3, 2020 EDT
Conoscenze di base: infrastruttura
Earned mag 2, 2020 EDT
[Deprecated] Advanced ML: ML Infrastructure
Earned mag 2, 2020 EDT
VM Migration
Earned mag 2, 2020 EDT
DEPRECATED Websites and Web Applications
Earned mag 2, 2020 EDT
[Deprecated] Cloud Logging
Earned mag 1, 2020 EDT
[DEPRECATED] Deploying Applications
Earned mag 1, 2020 EDT
Introduzione al ML: elaborazione del linguaggio
Earned apr 30, 2020 EDT
Kubernetes in Google Cloud
Earned apr 18, 2020 EDT
Security on AWS
Earned apr 12, 2020 EDT
Data Science on Google Cloud
Earned apr 12, 2020 EDT
Scientific Data Processing
Earned apr 12, 2020 EDT
DEPRECATED Applied Data: Blockchain
Earned apr 12, 2020 EDT
Baseline: dati, ML, AI
Earned apr 12, 2020 EDT
[DEPRECATED] Data Engineering
Earned apr 12, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned apr 12, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned apr 12, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned apr 12, 2020 EDT
Learn to secure your deployments on Google Cloud, including: how to use Cloud Armor bot management to mitigate bot risk and control access from automated clients; use Cloud Armor denylists to restrict or allow access to your HTTP(S) load balancer at the edge of the Google Cloud; apply Cloud Armor security policies to restrict access to cache objects on Cloud CDN and Google Cloud Storage; and mitigate common vulnerabilities using Cloud Armor WAF rules.
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Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.
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Completa il badge delle competenze di livello intermedio Ottimizza i costi per Google Kubernetes Engine per dimostrare le tue competenze nei seguenti ambiti: creazione e gestione di cluster multi-tenant, monitoraggio dell'utilizzo delle risorse per spazio dei nomi, configurazione della scalabilità automatica di cluster e pod per l'efficienza, impostazione del bilanciamento del carico per la distribuzione ottimale delle risorse e implementazione di probe di attività e idoneità per garantire l'integrità e la convenienza economica dell'applicazione.
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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.
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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.
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Guadagna un badge delle competenze completando il corso Creazione di una rete Google Cloud sicura, in cui scoprirai più risorse di networking per creare, scalare e proteggere le tue applicazioni su Google Cloud.
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In this quest you will get hands-on experience writing infrastructure as code with Terraform.
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Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.
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Ottieni il badge delle competenze introduttivo completando il corso con badge delle competenze Crea un sito web su Google Cloud. Questo corso si basa sulla serie Get Cooking in Cloud e tratta i seguenti argomenti:Deployment di un sito web su Cloud RunHosting di un'app web su Compute EngineCreazione, deployment e scalabilità del tuo sito web su Google Kubernetes EngineMigrazione da un'applicazione monolitica a un'architettura di microservizi utilizzando Cloud Build
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Complete the intermediate Develop Serverless Apps with Firebase skill badge course to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications.
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Completa il corso introduttivo con badge delle competenze Implementazione di Cloud Load Balancing per Compute Engine per dimostrare le tue competenze nei seguenti ambiti: creazione ed esecuzione del deployment di macchine virtuali in Compute Engine e configurazione di bilanciatori del carico di rete e delle applicazioni.
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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating and managing Git repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.
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Ottieni il corso intermedio con badge delle competenze Prepara i dati per le API ML su Google Cloud per dimostrare le tue competenze nei seguenti ambiti: pulizia dei dati con Dataprep di Trifacta, esecuzione delle pipeline di dati in Dataflow, creazione dei cluster ed esecuzione dei job Apache Spark in Managed Service for Apache Spark e richiamo delle API ML tra cui l'API Cloud Natural Language, l'API Google Cloud Speech-to-Text e l'API Video Intelligence.
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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.
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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.
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Ottieni un badge delle competenze completando il corso Configura un ambiente di sviluppo di app su Google Cloud, in cui imparerai a creare e connettere un'infrastruttura cloud incentrata sull'archiviazione utilizzando le funzionalità di base delle seguenti tecnologie: Cloud Storage, Identity and Access Management, Cloud Functions e Pub/Sub.
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Completa il corso intermedio con badge delle competenze Crea l'infrastruttura con Terraform su Google Cloud per dimostrare le tue competenze nei seguenti ambiti: principi di Infrastructure as Code (IaC) utilizzando Terraform, provisioning e gestione di risorse Google Cloud con configurazioni Terraform, gestione efficace dello stato (locale e remoto) e modularizzazione del codice Terraform per la riusabilità e l'organizzazione.
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Guadagna un badge delle competenze completando il corso Sviluppa la tua rete Google Cloud, in cui apprenderai diversi modi per eseguire il deployment e il monitoraggio delle applicazioni, tra cui: esplorare i ruoli IAM e aggiungere/rimuovere l'accesso ai progetti, creare reti VPC, eseguire il deployment e il monitoraggio delle VM di Compute Engine, scrivere query SQL, eseguire il deployment e il monitoraggio delle VM in Compute Engine ed eseguire il deployment delle applicazioni utilizzando Kubernetes con più approcci al deployment.
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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other.
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Completa il corso introduttivo con badge delle competenze Genera insight dai dati BigQuery per dimostrare le tue competenze nei seguenti ambiti: scrivere query SQL, eseguire query su tabelle pubbliche, caricare dati di esempio in BigQuery, risolvere i problemi di sintassi comuni con lo strumento di convalida query in BigQuery e creare report in Data Studio collegando ai dati di BigQuery.
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Java. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Java applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Java applications straight away.
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on GCP. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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This is the second of two courses of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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Se sei uno sviluppatore cloud principiante che vuole fare ancora pratica Google Cloud Essentials, questo corso fa al caso tuo. Acquisirai esperienza pratica attraverso lab specifici su Cloud Storage e altri servizi per applicazioni chiave come Monitoring e Cloud Functions. Svilupperai competenze preziose applicabili a qualsiasi iniziativa Google Cloud.
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.
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Cloud Logging is a fully managed service that performs at scale. It can ingest application and system log data from thousands of VMs and, even better, analyze all that log data in real time. In this fundamental-level Quest, you learn how to store, search, analyze, monitor, and alert on log data and events from Google Cloud. The labs in the Quest give you hands-on practice using Cloud Logging to maximize your learning experience and provide insight on how you can use Cloud Logging to your own Google Cloud environment.
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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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Non è un segreto che il machine learning sia uno dei campi in più rapida crescita nel settore tecnologico e la piattaforma Google Cloud è stata fondamentale per promuoverne lo sviluppo. Con le numerose API, Google Cloud dispone di uno strumento adeguato praticamente per qualsiasi job di machine learning. In questo corso introduttivo, farai pratica con il machine learning applicato all'elaborazione del linguaggio naturale partecipando ai lab che ti consentiranno di estrarre entità da un testo ed eseguire analisi del sentiment e della sintassi, nonché utilizzare l'API Speech-to-Text per la trascrizione.
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Kubernetes è il sistema di orchestrazione dei container più diffuso e Google Kubernetes Engine è stato progettato specificamente per supportare i deployment Kubernetes gestiti in Google Cloud. In questo corso di livello avanzato, potrai esercitarti nella configurazione di immagini e container Docker e nel deployment di applicazioni Kubernetes Engine complete. Grazie a questo corso, apprenderai le competenze pratiche necessarie per integrare l'orchestrazione dei container nel tuo workflow. Stai cercando un Challenge Lab pratico per dimostrare le tue abilità e convalidare le tue conoscenze? Dopo aver completato questo corso, termina il Challenge Lab aggiuntivo alla fine del corso Esegui il deployment di applicazioni Kubernetes su Google Cloud per ricevere un esclusivo badge digitale Google Cloud.
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This quest is designed to teach you how to apply AWS Identity and Access Management, in concert with several other AWS Services, to address real-world application and service security management scenarios.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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Big data, machine learning e intelligenza artificiale sono i principali argomenti di computing trattati attualmente, ma questi campi sono piuttosto specializzati ed è complicato reperire materiale introduttivo. Fortunatamente, Google Cloud offre servizi facili da usare in queste aree e con questo corso di livello introduttivo, in modo da poter fare i primi passi con strumenti come BigQuery, API Cloud Speech e Video Intelligence.
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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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