Course Overview
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Who should attend
Data Engineers
Prerequisites
Participants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.
Course Objectives
- Differentiate between ML, AI and deep learning.
- Discuss the use of ML API’s on unstructured data.
- Execute BigQuery commands from notebooks.
- Create ML models by using SQL syntax in BigQuery.
- Create ML models without coding by using AutoML
Outline: Smart Analytics, Machine Learning, and AI on Google Cloud (SAMLAI)
Module 1 - Introduction to Analytics and AI
Topics:
- What is AI?
- From ad hoc data analysis to data-driven decisions
- Options for ML models on Google Cloud
Objectives:
- Describe the relationship between ML, AI, and deep learning
- Identify ML options on Google Cloud
Module 2 - Prebuilt ML Model APIs for Unstructured Data
Topics:
- The difficulties of unstructured data
- ML APIs for enriching data
Objectives:
- Discuss challenges when working with unstructured data
- Identify ready-to-use ML API’s for unstructured data
Module 3 - Big Data Analytics with Notebooks
Topics:
- Defining notebooks
- BigQuery magic and ties to Pandas
Objectives:
- Introduce notebooks as a tool for prototyping ML solutions.
- Execute BigQuery commands from notebooks.
Module 4 - Production ML Pipelines
Topics:
- Ways to do ML on Google Cloud
- Vertex AI Pipelines
- TensorFlow Hub
Objectives:
- Describe options available for building custom ML models.
- Describe the use of tools like Vertex AI and TensorFlow Hub.
Module 5 - Custom Model Building with SQL in BigQuery ML
Topics:
- BigQuery ML for quick model building
- Supported models
Objectives:
- Create ML models by using SQL syntax in BigQuery.
- Demonstrate building different kinds of ML models by using BigQuery ML.
Module 6 - Custom Model Building with AutoML
Topics:
- Why use AutoML?
- AutoML Vision
- AutoML NLP
- AutoML Tables
Objectives:
- Explore various AutoML products used in machine learning.
- Identify ready-to-use ML API’s for unstructured data.