Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Equipping learners with practical skills in managing big data, machine learning (ML), and AI workloads using Google Cloud services.
- Gain expertise in Dataproc, Dataflow, BigQuery, and Dataprep
- Master core tools
- Understanding critical AI ethics frameworks
- Learn to build real-world solutions
- Learn to design scalable ETL pipelines
- Optimize Spark jobs and manage large datasets efficiently
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
The course "Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud" focuses on equipping learners with practical skills in managing big data, machine learning (ML), and AI workloads using Google Cloud services. Key components include:
- Big Data Services: Hands-on exploration of Dataproc (managed Spark/Hadoop clusters) and Dataflow (serverless ETL pipelines), with labs covering cluster creation, job execution, and pipeline development.
- Data Warehousing: Introduction to BigQuery, Google's enterprise-scale data warehouse, and Dataprep, a tool for data cleaning and preparation.
- Skill Development: Interactive labs (e.g., streaming pipelines with Dataflow templates, Python-based Dataflow workflows) and quizzes to validate understanding.
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud course Outline
Learn the Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Course
Looking to harness the power of cloud-based data and machine learning? The Google Cloud Computing Foundations: Data, ML, and AI course offers a hands-on dive into tools like Dataproc, Dataflow, and BigQuery. Through guided labs and real-world scenarios, you’ll learn to build scalable data pipelines, manage Spark clusters, and analyze massive datasets. Whether you're a developer, data analyst, or IT professional, this course provides the skills to tackle modern data challenges and earn a Google Cloud Skill Badge—a trusted credential in the industry. Ready to future-proof your career? Enroll now and start transforming data into actionable insights.
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Course Structure Includes:
Video: Introduction to what machine learning is, the terminology used, and its value proposition. Discover a variety of managed big data services in the cloud.
Lab: Hands-on exploration of Dataproc (managed Spark/Hadoop clusters) and Dataflow (serverless ETL pipelines), with labs covering cluster creation, job execution, and pipeline development.
Quiz: Quizzes to validate understanding.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
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- Average 7 Days to Practice & Pass
Description
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud" focuses on equipping learners with practical skills in managing big data, machine learning (ML), and AI workloads using Google Cloud services.
Pre-requisites
No Experience needed.
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The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
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