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
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.
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: Recommending Products 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.
(Friday) 9:00 am - 4:00 pm