Foundational Data, ML, and AI Tasks in Google Cloud

Learn how to use Google Cloud’s powerful tools for foundational data, machine learning (ML), and artificial intelligence (AI) tasks. This live training will cover the core capabilities of Google Cloud and how to implement key ML and AI operations effectively.

Data Science & Analytics

3 Hours

Description

This comprehensive 3-hour training session introduces participants to the foundational data processing, machine learning, and AI tasks in Google Cloud. We will explore essential tools such as BigQuery, Cloud Storage, Vertex AI, and AutoML to build and deploy intelligent solutions. Whether you're new to cloud technologies or looking to enhance your skills, this training will provide hands-on learning to navigate and leverage Google Cloud's powerful data, ML, and AI capabilities.

Course Objectives

Understand the core data storage and processing tools in Google Cloud (e.g., BigQuery, Cloud Storage).

Learn the foundational concepts and workflows in Machine Learning (ML) using Google Cloud.

Get hands-on experience with AutoML and Vertex AI to build, train, and deploy models.

Understand how to set up and manage AI pipelines, from data collection to model deployment.

Explore how Google Cloud integrates with existing data engineering and ML workflows.

Target Audience

Data Engineers and Analysts

Machine Learning Engineers

AI/ML Enthusiasts

Developers and IT professionals looking to explore cloud-based AI solutions

Anyone interested in building or deploying ML models on Google Cloud



Basic Understanding

Familiarity with basic data concepts and storage.

Basic understanding of Machine Learning (ML) principles (e.g., supervised vs. unsupervised learning).

Basic experience with cloud computing (ideal but not required).

No prior experience with Google Cloud is necessary.

Course Content

No sessions available.

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Foundational Data, ML, and AI Tasks in Google Cloud

Session 1: Introduction to Google Cloud Platform (GCP) for Data, ML, and AI

  1. Overview of GCP tools and services for data and machine learning
  2. GCP Console and project setup

Session 2: Google Cloud Storage & BigQuery

  1. Cloud Storage for storing data at scale
  2. BigQuery for data processing and analytics
  3. Importing data into BigQuery and running queries

Session 3: Introduction to Machine Learning in Google Cloud

  1. Understanding machine learning workflows
  2. Tools for ML on Google Cloud
  3. Overview of Vertex AI and AutoML services

Session 4: Building and Deploying Models with Vertex AI

  1. Data preparation and feature engineering
  2. Training, tuning, and deploying models using Vertex AI
  3. Model monitoring and optimization

Session 5: Leveraging AutoML for Rapid ML Model Development

  1. Introduction to AutoML tools
  2. Building custom models without deep ML expertise
  3. Training and evaluating AutoML models

Session 6: AI Pipelines and Automation

  1. Creating and managing AI pipelines for reproducible ML workflows
  2. Using Vertex AI Pipelines and Workflow Automation in Google Cloud

Session 7: Real-world Use Cases and Best Practices

  1. Common use cases for ML and AI in Google Cloud
  2. Best practices for data handling, model training, and deployment

Session 8: Q&A

  1. Open forum for questions and discussion

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