What you will learn

1.

Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.

2.

Design streaming pipelines with Dataflow and Pub/Sub.

3.

Analyze big data at scale with BigQuery.

4.

Identify different options to build machine learning solutions on Google Cloud.

5.

Describe a machine learning workflow and the key steps with Vertex AI.

6.

Build a machine learning pipeline using AutoML.

What's included?

Instructor LIVE training

An instructor will answer your questions

OFFICIAL

Google Cloud Content

Course reflects the latest authorised Google Cloud class contact

Hands on LABS

Real world hands on experience throughout the class. Supported by instructor.

Level

Beginner

Language

Delivered in English

Prerequisites

Familiarity with application development, systems operations, Linux operating systems is helpful in understanding the technologies covered.

Who this course is for

Data analysts, data scientists, and 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

Course TOPICS

1: Big Data and Machine Learning on Google Cloud

🔸This section explores the key components of Google Cloud's infrastructure. We introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.

2: Data Engineering for Streaming Data

🔸This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.

3: Big Data with BigQuery

🔸This ection introduces learners to BigQuery, Google's fully managed, serverless data warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models.

4: Machine Learning Options on Google Cloud

🔸This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.

5: The Machine Learning Workflow with Vertex AI

🔸This section focuses on the three key phases—data preparation, model training, and model preparation—of the machine learning workflow in Vertex AI. Learners can practice building a machine learning model with AutoML.