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.
What You'll LearnTOP
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.
- This course teaches participants the following skills:
Viewing outline for:
Recognize the data-to-AI lifecycle on Google Cloud
- Identify the connection between data engineering and machine learning
- Identify the different aspects of Google Clouds infrastructure.
- Identify the big data and machine learning products on Google Cloud.
- Lab: Exploring a BigQuery Public Dataset
- 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
- 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
- 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.
- 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
- 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.
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
- To get the most of out of this course, participants should have:
Who Should AttendTOP
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
- This class is intended for the following: