Big Data and Machine Learning Fundamentals

Course Introduction

• Recognize the data-to-AI lifecycle on Google Cloud

• Identify the connection between data engineering and machine learning

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.

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.

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.

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.

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. 

• 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

• 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