Google Cloud Big Data and Machine Learning Fundamental Course Overview:

Do you dream of transforming raw data into intelligent solutions that drive results? This course titled Google Cloud Big Data and Machine Learning Fundamentals is your career launchpad! You will master the data-to-AI lifecycle on Google Cloud, wielding powerful tools like BigQuery, Dataflow, Pub/Sub, and Vertex AI. 

You hone skills to create seamless data pipelines that stream in real-time with Dataflow and Pub/Sub, then conquer massive datasets with BigQuery’s unparalleled analytical power. Dive deep into building machine learning solutions on Google Cloud, exploring diverse options and workflows. Unravel the magic of Vertex AI, understand its key steps, and craft your own AutoML pipeline to automate model building. 

After completing Google Cloud Big Data & Machine Learning Fundamentals Training, students will be able to:

  • Decipher the data-to-AI lifecycle on Google Cloud and master its key stages.
  • Identify and utilize the major big data and machine learning products.
  • Design streaming pipelines that ingest and process data on the fly with Dataflow and Pub/Sub.
  • Conquer huge datasets with BigQuery's powerful analytics engine, unlocking hidden insights.
  • Explore diverse options for building machine-learning solutions tailored to your needs.
  • Demystify the machine learning workflow on Google Cloud and its key steps with Vertex AI.
  • Automate model building and unleash the power of AutoML to create your own machine-learning pipeline.

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Google Cloud Big Data & Machine Learning Fundamentals Certification: Key Features

  • Our Google Cloud Platform training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • GCP certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment during Google Cloud Platform training.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.
  • Being an official Google Cloud Platform Training Partner, we offer authored curriculum that are at par with industry standards.

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.

Prerequisites:

    Fundamental understanding of the following:
  • Database query language such as SQL
  • Data engineering workflow from extraction, transformation, and load to analysis, modelling, and deployment.
  • Machine learning models, such as supervised versus unsupervised models.

    • Learning objective of the course

    • Explain the key stages of the data-to-AI lifecycle on Google Cloud.
    • Identify and define the major Google Cloud products for big data and machine learning.
    • Design streaming data pipelines using Dataflow and Pub/Sub.
    • Implement real-time data ingestion and processing workflows.
    • Configure Pub/Subtopics and subscriptions for efficient data delivery.
    • Perform large-scale data analysis using BigQuery SQL queries.
    • Leverage BigQuery's features for data exploration, transformation, and visualization.
    • Apply best practices for efficient BigQuery query optimization.

    Why choose CloudThat as your Google Cloud Big Data and Machine Learning Fundamentals Training Partner?

    • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP.
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios.
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set.
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills.
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course.
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals.
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge.
    • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training.

    Course modules: Download Course Outline

    This section welcomes learners to the Big Data and Machine Learning Fundamentals course and provides an overview of the course structure and goals.

    Objectives:

    • Recognize the data-to-AI lifecycle on Google Cloud.
    • Identify the connection between data engineering and machine learning.

    This section explores the key components of Google Cloud's infrastructure. We introduce many big data and machine learning products and services that support the data-to-AI lifecycle on Google Cloud.

    Objectives:

    • Identify the different aspects of Google Cloud’s infrastructure.
    • Identify the big data and machine learning products on Google Cloud.

    Activities:

    • Lab: Exploring a BigQuery Public Dataset
    • Quiz

    This section explores the key components of Google Cloud's infrastructure. We introduce many big data and machine learning products and services that support the data-to-AI lifecycle on Google Cloud.

    Objectives:

    • Identify the different aspects of Google Cloud’s infrastructure.
    • Identify the big data and machine learning products on Google Cloud.

    Activities:

    • Lab: Exploring a BigQuery Public Dataset
    • Quiz

    This section introduces learners to BigQuery, Google's fully managed, serverless data warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models.

    Objectives:

    • Describe the essentials of BigQuery as a data warehouse.
    • Explain how BigQuery processes queries and stores data.
    • Define BigQuery ML project phases.
    • Build a custom machine learning model with BigQuery ML.

    Activities:

    • Lab: Predicting Visitor Purchases Using BigQuery ML
    • Quiz

    This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.

    Objectives:

    • Identify different options to build ML models on Google Cloud.
    • Define Vertex AI and its major features and benefits.
    • Describe AI solutions in both horizontal and vertical markets.

    Activities:

    • Lab: Predicting Visitor Purchases Using BigQuery ML
    • Quiz

    This section focuses on the machine learning workflow's three key phases—data preparation, model training, and model preparation—in Vertex AI. Learners can practice building a machine learning model with AutoML.

    Objectives:

    • Describe an ML workflow and the key steps.
    • Identify the tools and products to support each stage.
    • Build an end-to-end ML workflow using AutoML.

    Activities:

    • Lab: Vertex AI: Predicting Loan Risk with AutoML
    • Quiz

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    Course ID: 19125

    Course Price at

    $399 + 0% TAX
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    Frequently Asked Questions

    This course is ideal for data analysts, data scientists, business analysts, and individuals responsible for designing data pipelines, creating/maintaining machine learning models, querying datasets, and creating reports. It's also suitable for executives and IT decision-makers evaluating Google Cloud for data science purposes.

    Prospective learners should have a fundamental understanding of database query languages like SQL, knowledge of the data engineering workflow from extraction to deployment, and familiarity with machine learning models, such as supervised and unsupervised models.

    The introductory module of this course, dealing with Google Cloud fundamentals, big data, and machine learning, provides an overview of the course structure and goals. It aims to help learners recognize the data-to-AI lifecycle on Google Cloud and understand the critical connection between data engineering and machine learning.

    In this course named Google Cloud Big Data and Machine Learning Fundamentals, Module 3 introduces BigQuery as a fully managed, serverless data warehouse and explores BigQuery ML. Learners will understand BigQuery's essentials, query processing mechanisms, and the phases involved in BigQuery ML projects. Practical labs and quizzes aid in understanding.

    Module 4 of this course on Google Cloud platform Big Data & Machine Learning Fundamentals explores multiple options for building machine learning models on Google Cloud. It introduces Vertex AI, emphasizing its features, benefits, and application in horizontal and vertical markets. Practical labs and quizzes are included.

    Module 5 of this course, which deals with the Google Cloud Platform's big data and machine learning fundamentals, centers on the three key phases of the machine learning workflow – data preparation, model training, and model preparation – within Vertex AI. Learner’s practice building a machine learning model using AutoML and identify the tools and products supporting each stage through labs and quizzes.

    Google Cloud Machine Learning (GCP ML) is a comprehensive suite of tools and services offered by Google Cloud Platform (GCP) that allows you to build, train, deploy, and manage machine learning models. It enables organizations of all sizes to leverage machine learning without worrying about the underlying infrastructure or having extensive expertise in the field.

    Enquire Now