1 day course 
Partner of the Year 
Virtual, Private 
Certificate of Attendance As an award-winning Google Cloud Partner, we’ve been selected to run this Big Data & Machine Learning Fundamentals course.
Our instructors are industry experts who work with Google Cloud on a daily basis. Through virtual classes and practical labs, they will help you navigate key big data and machine learning processes and show you the role different services play in supporting the data-to-AI lifecycle.
Topics covered include BigQuery, Dataflow, Pub / Sub, Apache Beam, Looker, Data Studio, Document AI, Contact Center AI (CCAI) and Kubernetes Engine, among others.
Our Google Cloud Fundamentals: Big Data & Machine Learning course is delivered via Virtual Classroom. We also offer it as a private training session that can be delivered virtually or at a location of your choice in the UK.
Course overview
Who should attend:
This course is suitable 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, visualising query results, and creating reports
- Executives and IT decision makers evaluating Google Cloud for use by data scientists
What you'll learn:
By the end of this course, you will be able to:
- Recognise 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
- Analyse big data at scale with BigQuery
- Identify different options to build machine learning solutions on Google Cloud.
- Describe a machine learning workflow and the key steps with Vertex AI.
- Build a machine learning pipeline using AutoML
Prerequisites
To get the most out of this course, you should have basic understanding of one or more of the following:
- A database query language such as SQL
- Aspects of the data engineering workflow – from extract, transform and load, to analysis, modelling, and deployment
- Machine learning models, such as supervised versus unsupervised models
Course agenda
- Recognise the data-to-AI lifecycle on Google Cloud
- Identify the connection between data engineering and machine learning
- Identify the different aspects of Google Cloud’s infrastructure
- Identify the big data and machine learning products on Google Cloud
- Lab: Exploring a BigQuery Public Dataset
- Describe an end-to-end streaming data workflow from ingestion to data visualisation
- Identify modern data pipeline challenges and how to solve them at scale with Dataflow
- Build collaborative real-time dashboards with data visualisation tools
- Lab: Creating a Streaming Data Pipeline for a Real-time Dashboard with Dataflow
- Quiz
- Describe the essentials of BigQuery as a data warehouse
- Explain how BigQuery processes queries and stores data
- Define BigQuery ML project phases
- Build a custom machine learning model with BigQuery ML
- Lab: Predicting Visitor Purchases Using BigQuery ML
- Identify different options to build ML models on Google Cloud
- Define Vertex AI and its major features and benefits
- Describe AI solutions in both horizontal and vertical markets
- Describe a ML workflow and the key steps
- Identify the tools and products to support each stage
- Build an end-to-end ML workflow using AutoML
- Lab: Vertex AI: Predicting Loan Risk with AutoML