When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Google Cloud Big Data and Machine Learning Fundamentals
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a...
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$900 USD
GSA
$554.41
Course Code
GCP-BD-ML
Duration
1 day
Available Formats
Classroom, Virtual
“Training was really good and loved having examples to test against the cloud. The training format was also really nice with live interaction but comfort of being at home for the training.”
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Skills Gained
This course teaches participants the following skills:
- Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
- Design streaming pipelines with Dataflow and Pub/Sub.
- Analyze big data at scale with BigQuery.
- Identify different options to build machine learning solutions on Google Cloud.
- Describe a machine learning workflow and the key steps with Vertex AI.
- Build a machine learning pipeline using AutoML.
Who Can Benefit
This class is intended for the following:
- Data analysts, data scientists, and business analysts who are getting started with Google Cloud
- Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports
- Executives and IT decision makers evaluating Google Cloud for use by data scientists
Prerequisites
To get the most of out of this course, participants should have:
- Database query language such as SQL
- Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
- Machine learning models such as supervised versus unsupervised models
Course Details
Course Outline
Module 0: Course Introduction
- Recognize the data-to-AI lifecycle on Google Cloud
- Identify the connection between data engineering and machine learning
Module 1: Big Data and Machine Learning on Google Cloud
- Identify the different aspects of Google Cloud’s infrastructure.
- Identify the big data and machine learning products on Google Cloud.
- Lab: Exploring a BigQuery Public Dataset
- Quiz
Module 2: Data Engineering for Streaming Data
- Describe an end-to-end streaming data workflow from ingestion
- to data visualization.
- Identify modern data pipeline challenges and how to solve them at scale
- with Dataflow.
- Build collaborative real-time dashboards with data visualization tools.
- Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
- Quiz
Module 3: Big Data with BigQuery
- Describe the essentials of BigQuery as a data warehouse.
- Explain how BigQuery processes queries and stores data.
- Define BigQuery ML project phases.
- Build a custom machine learning model with BigQuery ML.
- Lab: Predicting Visitor Purchases Using BigQuery ML
- Quiz
Module 4: Machine Learning Options on Google Cloud
- Identify different options to build ML models on Google Cloud.
- Define Vertex AI and its major features and benefits.
- Describe AI solutions in both horizontal and vertical markets.
- Quiz
Module 5: The Machine Learning Workflow with Vertex AI
- Describe a ML workflow and the key steps.
- Identify the tools and products to support each stage.
- Build an end-to-end ML workflow using AutoML.
- Lab: Vertex AI: Predicting Loan Risk with AutoML
- Quiz
Module 6: Course Summary
- This section reviews the topics covered in the course and provides additional resources for further learning.
- Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.
