Formation Google Cloud Big Data and Machine Learning Fundamentals

Module 1: Big Data and Machine Learning on Google Cloud

Explore key components of Google Cloud infrastructure.

Introduction to various big data and machine learning products and services.

Objectives: Identify different aspects of Google Cloud infrastructure, recognize big data and machine learning products.

Module 2: Data Engineering for Streaming Data

Introduction to Google Cloud's solution for managing streaming data.

End-to-end data workflow overview.

Objectives: Describe an end-to-end data streaming workflow, identify challenges in modern data pipelines, create real-time collaborative dashboards.

Module 3: Big Data with BigQuery

Introduction to BigQuery, Google's serverless data warehouse.

Exploration of BigQuery ML.

Objectives: Describe basics of BigQuery, explain its processing and data storage, define BigQuery ML project phases, create custom machine learning models.

Module 4: Machine Learning Options on Google Cloud

Explore four different options for creating machine learning models.

Introduction to Vertex AI.

Objectives: Identify various options for creating ML models, define Vertex AI, describe AI solutions in horizontal and vertical markets.

Module 5: Machine Learning Workflow with Vertex AI

Focus on three key phases of the ML workflow in Vertex AI.

Hands-on practice creating a machine learning model with AutoML.

Objectives: Describe ML workflow stages, identify tools supporting each stage, create an end-to-end ML workflow using AutoML.

Module 6: Course Synthesis

Review of course topics.

Provide additional resources for further learning.

Objectives: Describe the data-to-AI lifecycle on Google Cloud, identify key big data and machine learning products.