Course Overview:

This course provides a comprehensive introduction to Google Cloud’s smart analytics and machine learning services. It is designed for learners who want to build, train, and deploy machine learning models using Google Cloud’s powerful tools. The curriculum covers a range of topics, from utilizing prebuilt APIs for unstructured data to building custom models with SQL in BigQuery ML and automating the process with Vertex AI AutoML. Learners will gain practical, hands-on experience in developing end-to-end ML pipelines on the Google Cloud Platform. 

After completing this course, participants will be able to:

  • Describe the various options for machine learning on Google Cloud.
  • Utilize prebuilt ML model APIs for analyzing unstructured data.
  • Perform big data analytics using notebooks in Vertex AI.
  • Build and run production ML pipelines.
  • Create custom regression models using SQL in BigQuery ML.
  • Develop custom models using Vertex AI AutoML.

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Key Features:

  • Practical, Hands-On Labs: The course includes interactive labs where you’ll get hands-on experience with core Google Cloud products. 

  • End-to-End ML Lifecycle Coverage: Learn the full workflow of a machine learning project, from initial data analysis to deploying a production-ready model. 

  • Tool-Centric Approach: Gain practical experience with key services like BigQuery ML, Vertex AI, and prebuilt ML APIs.

  • Flexible Learning Paths: Explore different approaches to model building, including SQL-based regression models and automated model creation with Vertex AI AutoML. 

  • Intermediate-Level Curriculum: Designed for individuals with some prior understanding of data concepts and an interest in applying machine learning to solve real-world problems. 

Who should Attend?

  • Data professionals
  • Data analysts
  • Data scientists
  • Machine learning engineers who want to leverage Google Cloud's robust ecosystem to build and manage ML solutions.

Prerequisites:

To enroll in this course, it is recommended to have:
  • A basic understanding of data concepts and familiarity with Google Cloud is recommended.

    • Why choose CloudThat as your 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.
    • 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.

    Learning objective:

    • After completing this course, students will be able to describe the data analytics workflow on Google Cloud and apply various services like BigQuery, Vertex AI, and prebuilt ML APIs to solve real-world problems.

    Course Outline: Download Course Outline

    Topics

    • Module introduction
    • What is AI?
    • From ad-hoc data analysis to data-driven decisions
    • Options for ML models on Google Cloud

    Objectives

    • Define AI and ML concepts.
    • Explain the role of analytics in decision-making.
    • Identify options for ML models on Google Cloud.

    Activities

    • Lecture
    • Demo

    Topics

    • Module introduction
    • Unstructured data is hard
    • ML APIs for enriching data

    Objectives

    • Understand challenges of unstructured data.
    • Use Google Cloud ML APIs (Vision, Natural Language, Translation) to enrich data.
    • Apply prebuilt APIs for text and image processing

    Activities

    • Lecture
    • Demo
    • Lab

    Topics

    • Module introduction
    • What’s a Notebook?
    • BigQuery magic and ties to Pandas

    Objectives

    • Explain the purpose of notebooks in data science workflows.
    • Use BigQuery in JupyterLab for data analysis.
    • Integrate BigQuery with Python libraries like Pandas.

    Activities

    • Lecture
    • Demo
    • Lab

    Topics

    • Module introduction
    • Ways to do ML on Google Cloud
    • Vertex AI Pipelines
    • TensorFlow Hub

    Objectives

    • Describe ML pipeline components and orchestration.
    • Build and run pipelines using Vertex AI
    • Monitor and troubleshoot ML workflows.

    Activities

    • Lecture
    • Demo
    • Lab

    Topics

    • Module introduction
    • BigQuery ML for quick model building
    • Supported models
    • Summary

    Objectives

    • Use BigQuery ML for rapid model development
    • Train and evaluate models using SQL.
    • Explore supported models and use cases.

    Activities

    • Lecture
    • Demo
    • Lab

    Topics

    • Module introduction
    • Why AutoML?
    • AutoML Vision
    • AutoML Natural Language
    • AutoML Tables
    • Summary

    Objectives

    • Explain AutoML capabilities for structured and unstructured data.
    • Train models using AutoML Vision, Natural Language, and Tables.
    • Deploy and evaluate AutoML models.

    Activities

    • Lecture
    • Lab

    Topics

    • Course summary

    Objectives

    • Review key concepts from all modules.
    • Identify next steps for learning and certification.
    • Access additional resources for continued learning

    Activities

    • Review
    • Wrap-up
    • Q&A

    Certification Details:

      CloudThat Course Completion Certificate

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    Course ID: 26077

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    FAQs:

    This course focuses on using Google Cloud tools to build and deploy smart analytics and machine learning solutions, from using prebuilt APIs to creating custom models.

    This is an intensive, 1-day, instructor-led training that combines theory with hands-on labs and demos to ensure an interactive learning experience.

    Learners will complete multiple labs, including tasks like running pipelines on Vertex AI, predicting bike trip duration with BigQuery ML, and using the Natural Language API for text classification.

    Yes, a key module covers performing big data analytics using JupyterLab notebooks on Vertex AI.

    The course teaches you to build custom machine learning models, specifically regression models, directly with SQL in BigQuery ML.

    The course emphasizes real-world applications, helping learners use data and machine learning to answer business questions and support data-driven decision-making.

    This course serves as an introductory foundation. Learners interested in advanced topics like deep learning or more complex modeling can pursue follow-up courses.

    Yes, one of the modules is dedicated to building custom models using the automated machine learning capabilities of Vertex AI AutoML.

    A basic understanding of data types, data analysis concepts, and foundational knowledge of the Google Cloud Platform is recommended, but not strictly required.

    Upon completing all modules and hands-on labs, learners typically receive a course completion certificate from Google Cloud Learning Services.

    Enquire Now