Course Overveiw

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 based on the different goals of users, including data scientists, AI developers, and ML engineers.

After completing this course, students will be able to:

  • Recognize the data-to-AI technologies and tools offered by Google Cloud.
  • Use generative AI capabilities in applications.
  • Choose between different options to develop an AI project on Google Cloud.
  • Build ML models end-to-end by using Vertex AI

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Key Features:

  • Vertex AI (Unified ML Platform)

    • End-to-end AI development for building, training, and deploying ML models.
    • Supports AutoML for low-code model training and custom ML models for advanced users.
    • Integrated MLOps for monitoring and automating workflows.

     

  • AutoML (No-Code & Low-Code ML)

    • Allows users to train custom ML models without extensive coding.
    • Supports tabular, vision, language, and structured data models.
    • Automates feature engineering and hyperparameter tuning.

  • AI APIs (Pre-Trained AI Models)

    • Vision AI – Image recognition, object detection, OCR (Optical Character Recognition).
    • Natural Language AI – Sentiment analysis, entity recognition, and text classification.
    • Translation AI – Real-time and batch language translation.
    • Speech-to-Text & Text-to-Speech – Audio transcription and text synthesis.

  • BigQuery ML (SQL-Based Machine Learning)

    • Enables users to build and run ML models using SQL.
    • Supports linear regression, logistic regression, time-series forecasting, and deep learning models.
    • Direct integration with BigQuery for large-scale data analysis.

     

  • TensorFlow & PyTorch Support

    • Generative AI & Large Language Models (LLMs)
    • Use case: Duet AI, Model Garden, Generative AI Studio
    • Lab: Exploring Generative AI Studio
    • Quiz & Reading materials

  • MLOps & AI Pipelines

    • Tools for automating and monitoring ML workflows.
    • Model versioning, deployment, and real-time performance tracking.
    • Integration with Kubeflow for Kubernetes-based AI workflows.

  • AI Infrastructure (Scalability & Performance)

    • High-performance GPUs and TPUs for training and inference.
    • Serverless AI solutions with managed compute resources.
    • Distributed training for large datasets and deep learning models.

  • Explainable AI & Responsible AI Tools

    • Model interpretability with Explainable AI for better decision-making.
    • Bias detection and fairness analysis with tools like the What-If Tool.
    • Privacy and compliance features for ethical AI development.

     

  • Google Cloud Data Ecosystem Integration

    • Seamless connectivity with BigQuery, Cloud Storage, and Dataflow.
    • AI-driven analytics and data processing for better insights.
    • Integration with Looker and Data Studio for visualization.

  • Cost-Effective & Pay-As-You-Go Pricing

    • Free tiers available for AI APIs and ML services.
    • Pay only for compute and storage used, optimizing cost efficiency.
    • Auto-scaling features to adjust resources based on demand.

Who Should Attend the training ?

  • Developers & Data Scientists
  • IT & Cloud Professionals
  • Students & Enthusiasts

Prerequisites:

Candidates have a basic understanding of cloud computing concepts like infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).

Course Outline Download Course Outline

Topics

  • Course introduction

Objectives

  • Define the course goal.
  • Recognize the course objectives.

Topics

  • Why Google?
  • AI/ML framework on Google Cloud
  • Google Cloud infrastructure
  • Data and AI products
  • ML model categories
  • BigQuery ML
  • Lab introduction: BigQuery ML

Objectives

  • 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-to-AI lifecycle.
  • Build an ML model with BigQueryML to bring data to AI.

Activities

  • 1 Lab 1 Quiz

Topics

  • AI development options
  • Pre-trained APIs
  • Vertex AI
  • AutoML
  • Custom training
  • Lab introduction: Natural Language API

Objectives

  • 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.

Activities

  • 1 Lab 1 Quiz

Topics

  • How a machine learns
  • ML workflow
  • Data preparation
  • Model development
  • Model serving
  • MLOps and workflow automation
  • Lab introduction: AutoML

Objectives

  • 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.

Activities

  • 1 Lab 1 Quiz

Topics

  • Generative AI and LLM
  • Generative AI use case: Duet AI
  • Model Garden
  • Generative AI Studio
  • AI solutions
  • Lab introduction: Generative AI Studio

Objectives

  • Define generative AI and large language models.
  • Use generative AI capabilities in AI development.
  • Recognize the AI solutions and the embedded generative AI features.

Activities

  • 1 Lab 1 Quiz

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Course ID: 24514

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AI refers to the simulation of human intelligence in machines, enabling them to learn, reason, and make decisions.

ML is a subset of AI that allows systems to learn patterns from data and improve performance without being explicitly programmed

Google Cloud offers managed services like Vertex AI, AutoML, BigQuery ML, and AI APIs, reducing infrastructure complexity and enabling scalable ML solutions.

Vertex AI is Google Cloud’s unified AI/ML platform that helps build, deploy, and manage ML models efficiently.

Google Cloud provides APIs like Vision AI, Natural Language AI, Translation AI, Speech-to-Text, and Text-to-Speech.

AutoML is a Google Cloud tool that allows users to train custom ML models with minimal coding using pre-built templates.

You can begin by creating a Google Cloud account, enabling AI services, and exploring AI APIs or Vertex AI via the Google Cloud Console.

Google Cloud supports Python, Java, and other languages via AI SDKs and APIs, with TensorFlow and PyTorch being the most common ML frameworks.

BigQuery ML enables users to create and run ML models using standard SQL queries directly within BigQuery.

Pricing varies by service, with free tiers available for some AI APIs and pay-as-you-go options for compute and training services.

Techniques include feature engineering, hyperparameter tuning, using GPUs/TPUs for training, and leveraging Google Cloud’s AutoML capabilities.

Enquire Now