What Will You Learn?
Identify 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 and dDesign streaming pipelines with Dataflow and Pub/Sub.
Identify different options to build machine learning solutions on Google Cloud.
Describe a machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.
About This Course
Provider: Coursera
Format: Online
Duration: 9 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: By the end of this free course, you will be able to navigate the data-to-AI lifecycle on Google Cloud, proficiently utilizing Vertex AI for building robust big data pipelines and machine learning models.
Course Prerequisites: NA
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Google Cloud
Key Topics: Google Cloud Platform, Cloud Computing, Bigquery, Tensorflow
Topic Covered:
- - Course introduction
- - Google Cloud infrastructure
- - Compute
- - Storage
- - The history of big data and ML products
- - Big data and ML product categories
- - Getting Started with Google Cloud Platform and Qwiklabs
- - Big data challenges
- - Message-oriented architecture
- - Designing streaming pipelines with Apache Beam
- - Implementing streaming pipelines on Cloud Dataflow
- - Visualization with Looker
- - Visualization with Looker Studio
- - Storage and analytics
- - Demo: Querying TB of data in seconds
- - Introduction to BigQuery ML
- - Using BigQuery ML to predict customer lifetime value
- - BigQuery ML project phases
- - BigQuery ML key commands
- - Options to build ML models
- - Pre-built APIs
- - AutoML
- - Custom training
- - Vertex AI
- - AI Solutions
- - Data preparation
- - Model training
- - Model evaluation
- - Model deployment and monitoring
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