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This certification & training 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.
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What You'll Learn

  • 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.

Who Should Attend

  • 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.
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Prerequisites

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.

Learning Journey

Want to boost your career in Google Cloud? Click on the roles below to see the learning pathways, specially designed to give you the skills to succeed.

Module 1: Introducing Google Cloud Platform
  • Google Platform Fundamentals Overview.
  • Google Cloud Platform Big Data Products.
  • Lab: Sign up for Google Cloud Platform.
Module 2: Compute and Storage Fundamentals
  • CPUs on demand (Compute Engine).
  • A global file system (Cloud Storage).
  • Cloud Shell.
  • Lab: Set up an Ingest-Transform-Publish data processing pipeline.
Module 3: Data Analytics on the Cloud
  • Stepping stones to the cloud.
  • Cloud SQL: your SQL database on the cloud.
  • Lab: Importing data into CloudSQL and running queries.
  • Spark on Dataproc.
  • Lab: Machine Learning Recommendations with Spark on Dataproc.
Module 4: Scaling Data Analysis
  • Fast random access.
  • Datalab.
  • BigQuery.
  • Lab: Build a Machine Learning Dataset.
Module 5: Machine Learning
  • Machine Learning with TensorFlow.
  • Lab: Carry out ML with TensorFlow.
  • Pre-built models for common needs.
  • Lab: Employ ML APIs.
Module 6: Data Processing Architectures
  • Message-oriented architectures with Pub/Sub.
  • Creating pipelines with Dataflow.
  • Reference architecture for real-time and batch data processing.
Module 7: Summary
  • Why GCP?.
  • Where to go from here.
  • Additional Resources
Associated with Machine Learning Engineer & Professional Data Engineer Certification.

Frequently Asked Questions (FAQs)

  • Why get Google Cloud Platform (GCP) certified?

    Google Cloud certifications validate your expertise in cloud technologies and your proficiency in using Google Cloud Platform's vast array of services.

    These certifications are recognized globally and highly sought after by employers, as they demonstrate your ability to design, develop, and manage scalable and secure cloud solutions on GCP.

    Google Cloud-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    Google Cloud offers a variety of certification exams across different levels (Foundational, Associate, and Professional) covering various job roles and specializations.

    The exams typically consist of multiple-choice and multiple-select questions, as well as scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is Google Cloud Platform (GCP) certification valid for?

    Most Google Cloud certifications, including Professional-level certifications, are valid for two years from the date of passing the exam.

    The Cloud Digital Leader and Associate Cloud Engineer certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the same exam or an equivalent higher-level certification exam before your current certification expires.

    You will receive a notification from Google Cloud prior to your certification's expiration date.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

Keep Exploring

Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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