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Overview

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.
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What You'll Learn

  • Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.
  • Understand when to use AutoML and BigQuery ML.
  • Create Vertex AI-managed datasets.
  • Add features to the Vertex AI Feature Store.
  • Describe Analytics Hub, Dataplex, and Data Catalog.
  • Describe how to improve model performance.
  • Create Vertex AI Workbench user-managed notebook, build a custom training job, and deploy it by using a Docker container.
  • Describe batch and online predictions and model monitoring.
  • Describe how to improve data quality and explore your data.
  • Build and train supervised learning models.
  • Optimize and evaluate models by using loss functions and performance metrics.
  • Create repeatable and scalable train, eval, and test datasets.
  • Implement ML models by using TensorFlow or Keras.
  • Understand the benefits of using feature engineering.
  • Explain Vertex AI Model Monitoring and Vertex AI Pipelines.

Who Should Attend

  • Aspiring machine learning data analysts, data scientists, and data engineers
  • Learners who want exposure to ML and use Vertex AI, AutoML, BigQuery ML, Vertex AI Feature Store, Vertex AI Workbench, Dataflow, Vertex AI Vizier for hyperparameter tuning, and TensorFlow/Keras
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Prerequisites

  • Some familiarity with basic machine learning concepts.
  • Basic proficiency with a scripting language - Python preferred.

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.

  • Data Scientist
  • Machine Learning Engineer
  • MLOps Engineer

Data Scientist

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Fundamentals

GCPML : Machine Learning on Google Cloud

Course Duration

5 Days

Fees

₹ 75,000.00

Certification

Intermediate

Expert

Machine Learning Engineer

icon-trainocate-certified.png

Fundamentals

GCPML : Machine Learning on Google Cloud

Course Duration

5 Days

Fees

₹ 75,000.00

Certification

Intermediate

MLOF : MLOps (Machine Learning Operations) Fundamentals

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

Expert

MPGC : ML Pipelines on Google Cloud

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

Machine Learning Engineer

icon-trainocate-certified.png

Fundamentals

GCPML : Machine Learning on Google Cloud

Course Duration

5 Days

Fees

₹ 75,000.00

Certification

Intermediate

MLOF : MLOps (Machine Learning Operations) Fundamentals

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

Expert

MPGC : ML Pipelines on Google Cloud

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

MLOps Engineer

icon-trainocate-certified.png

Fundamentals

GCPML : Machine Learning on Google Cloud

Course Duration

5 Days

Fees

₹ 75,000.00

Certification

Intermediate

MLOF : MLOps (Machine Learning Operations) Fundamentals

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

Expert

MPGC : ML Pipelines on Google Cloud

Course Duration

1 Day

Fees

₹ 15,000.00

Certification

Introduction to AI and Machine Learning on Google Cloud

  • Recognize the AI/ML framework on Google Cloud.
  • Identify the major components of Google Cloud infrastructure.
  • Define the data and ML products on Google Cloud and how they support the data[1]to-AI lifecycle.
  • Build an ML model with BigQueryML to bring data to AI.
  • Define different options to build an ML model on Google Cloud.
  • Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
  • Use the Natural Language API to analyze text.
  • Define the workflow of building an ML model.
  • Describe MLOps and workflow automation on Google Cloud.
  • Build an ML model from end-to-end by using AutoML on Vertex AI.
  • Define generative AI and large language models.
  • Use generative AI capabilities in AI development.
  • Recognize the AI solutions and the embedded generative AI features.

Launching into Machine Learning

  • Describe how to improve data quality.
  • Perform exploratory data analysis.
  • Build and train supervised learning models.
  • Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.
  • Describe BigQuery ML and its benefits.
  • Optimize and evaluate models by using loss functions and performance metrics.
  • Mitigate common problems that arise in machine learning.
  • Create repeatable and scalable training, evaluation, and test datasets.

TensorFlow on Google Cloud

  • Create TensorFlow and Keras machine learning models.
  • Describe the TensorFlow main components.
  • Use the tf.data library to manipulate data and large datasets.
  • Build a ML model that uses tf.keras preprocessing layers.
  • Use the Keras Sequential and Functional APIs for simple and advanced model creation.
  • Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.

Feature Engineering

  • Describe Vertex AI Feature Store.
  • Compare the key required aspects of a good feature.
  • Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
  • Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.

Machine Learning in the Enterprise

  • Understand the tools required for data management and governance.
  • Describe the best approach for data preprocessing: From providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.
  • Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.
  • Describe hyperparameter tuning by using Vertex AI Vizier to improve model performance.
  • Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
  • Describe the benefits of Vertex AI Pipelines.
  • Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.
This course is not associated with any 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

Training Schedule

Exam & Certification

Frequently Asked Questions

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