Google Cloud Big Data and Machine Learning Fundamentals
This course will introduce you to Google Cloud’s big data and machine learning functions. You’ll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.
- Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
- Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
- Employ BigQuery and Cloud SQL to carry out interactive data analysis.
- Choose between different data processing products on Google Cloud.
- Create ML models with BigQuery ML, ML APIs, and AutoML.
- Data analysts, data scientists, 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
Roughly one year of experience with one or more of the following:
- A common query language such as SQL
- Extract, transform, and load activities
- Data modeling
- Machine learning and/or statistics
- Programming in Python
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Introduction to Google Cloud
- Identify the different aspects of Google Cloud’s infrastructure
- Identify the big data and ML products that form Google Cloud
Module 2: Product Recommendations Using Cloud SQL and Spark
- Review how businesses use recommendation models
- Evaluate how and where you will compute and store your housing rental model results
- Analyze how running Hadoop in the cloud with Dataproc can enable scale
- Evaluate different approaches for storing recommendation data off-cluster
Module 3: Predicting Visitor Purchases Using BigQuery ML
- Analyze big data at scale with BigQuery
- Learn how BigQuery processes queries and stores data at scale
- Walkthrough key ML terms: features, labels, training data
- Evaluate the different types of models for structured datasets
- Create custom ML models with BigQuery ML
Module 4: Real-time Dashboards with Pub/Sub, Dataflow, and Google Data Studio
- Identify modern data pipeline challenges and how to solve them at scale with Dataflow
- Design streaming pipelines with Apache Beam
- Build collaborative real-time dashboards with Data Studio
Module 5: Deriving Insights from Unstructured Data using Machine Learning
- Evaluate how businesses use unstructured ML models and how the models work
- Choose the right approach for machine learning models between pre-built and custom
- Create a high-performing custom image classification model with no code using AutoML
Module 6: Summary
- Recap of key learning points