Google Cloud Fundamentals: Big Data & Machine Learning

This course will provide you with a comprehensive introduction to the big data and machine learning functions provided by Google Cloud.
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1 day course
Supporting material
Google Cloud Partner of the Year
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Jellyfish has been selected by Google to facilitate the delivery of this one-day instructor led course. Through expert guidance and practical labs, you’ll gain an overview of Google Cloud and a detailed view of its data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.

You will see the Apache SparkML recommendation model and how it can run in the cloud with Dataproc and Cloud SQL as well as the foundations of BigQuery and big data analysis at scale. You'll also learn about Auto-scaling streaming data to ingest, process, and visualize data on a dashboard, as well as the foundations of message-oriented architecture and the pitfalls to avoid when designing and implementing modern data pipelines.

Finally, you will create a custom ML model from scratch and learn how to leverage and extend pre-built ML models for image classification.

Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed. As a Google Cloud Partner, we’ll help equip you with the skills to process data efficiently and effectively to scale, using a range of specially designed Google Products on Google Cloud.

Course overview
Who should attend:
This course is intended for the following participants:
  • 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
Walk away with the ability to:
  • Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud
  • Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop / Pig / Spark / Hive workloads to Google Cloud
  • Employ BigQuery and Cloud SQL to carry out interactive data analysis
  • Choose between different data processing products on Google Cloud
  • Create ML models with BigQuery ML, ML APIs, and AutoML
To get the most of out of this course, you should have roughly one year of experience with one or more of the following:
  • A common query language such as SQL
  • Data modeling, extract, transform, load activities
  • Machine learning and / or statistics or Programming in Python
Course agenda
Module 1: Introduction to Google Cloud
  • Identify the different aspects of Google Cloud’s infrastructure
  • Identify the big data and ML products that form Google Cloud
Module 2: Product Recommendations Using Cloud SQL and Spark
  • Review how businesses use recommendation models
  • Evaluate how and where you will compute and store your housing rental model results
  • Analyse how running Hadoop in the cloud with Dataproc can enable scale
  • Evaluate different approaches for storing recommendation data off-cluster
Module 3: Predicting Visitor Purchases Using BigQuery ML
  • Analyse big data at scale with BigQuery
  • Learn how BigQuery processes queries and stores data at scale
  • Walk through key ML terms: features, labels, training data
  • Evaluate the different types of models for structured datasets
  • Create custom ML models with BigQuery ML
Module 4: Real-time Dashboards with Pub / Sub, Dataflow, and Google Data Studio
  • Identify modern data pipeline challenges and how to solve them at scale with Dataflow
  • Design streaming pipelines with Apache Beam
  • Build collaborative real-time dashboards with Data Studio
Module 5: Deriving Insights from Unstructured Data Using Machine Learning
  • Evaluate how businesses use unstructured ML models and how the models work
  • Choose the right approach for machine learning models between pre-built and custom
  • Create a high-performing custom image classification model with no code using AutoML
Module 6: Summary
  • Recap of key learning points
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