Big Data and Machine Learning Fundamentals
Description
TASTE OF 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.
Audience and prerequisites
This class is intended for the following participants:
- 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
Objectives
This course teaches participants the following skills:
- Identify the purpose and value of Google Cloud products and services
- Interact with Google Cloud services
- Describe the ways customers have used Google Cloud
- Use big data and ML products on Google Cloud: App Engine, Google Kubernetes Engine and Compute Engine
- Use BigQuery, Google's managed data store for statistics
Topics
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
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
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
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.
Module 5: The Machine Learning Workflow with Vertex AI
- Describe an 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
