Foundational Data, ML, and AI Tasks in Google Cloud
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Training TypeLive Training
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CategoryMachine Learning
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Duration3 Hours
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Rating4.9/5
Course Introduction
About the Course
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 Objective
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.
Who is the 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 Knowledge
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.
Available Batches
| 21 Feb 2025 | Fri ( 1 Day ) | 02:00 PM - 05:00 PM (Eastern Time) |
| 20 Mar 2025 | Thu ( 1 Day ) | 02:00 PM - 05:00 PM (Eastern Time) |
Pricing
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Overview of GCP tools and services for data and machine learning
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GCP Console and project setup
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Cloud Storage for storing data at scale
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BigQuery for data processing and analytics
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Importing data into BigQuery and running queries
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Understanding machine learning workflows
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Tools for ML on Google Cloud
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Overview of Vertex AI and AutoML services
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Data preparation and feature engineering
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Training, tuning, and deploying models using Vertex AI
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Model monitoring and optimization
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Introduction to AutoML tools
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Building custom models without deep ML expertise
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Training and evaluating AutoML models
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Creating and managing AI pipelines for reproducible ML workflows
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Using Vertex AI Pipelines and Workflow Automation in Google Cloud
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Common use cases for ML and AI in Google Cloud
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Best practices for data handling, model training, and deployment
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Open forum for questions and discussion