Introduction to Data Science on the Google Cloud


Course Number: GC-146

Duration: 5 days (32.5 hours)

Format: Live, hands-on

Data Science on GCP Training Overview

This Data Science on the Google Cloud training course teaches engineers, data scientists, statisticians, and other quantitative professionals how to use the Google Cloud Platform for data science projects. Attendees learn how to use Jupyter notebooks with Google’s latest cloud tools for data science.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some Cloud courses are available as live, online classes for individuals.

Objectives

  • Utilize Google Cloud Vertex AI (a managed Jupyter notebook service from Google Cloud) and take advantage of the features of the cloud for notebook computing.
    • Use the interface to run different notebook kernels and virtual machines in Vertex AI.
    • Explore Google Cloud Vertex AI sample notebooks and new use cases of data science on the cloud.
    • Use GitHub integration and Git via the graphical JupyterLab interface.
    • Write your own notebooks and use the Vertex AI notebook executor to schedule and parallelize running your notebooks as parameterized compute jobs.
  • Use Google Cloud Public Datasets to get experience working with large datasets in the cloud (GB and TB scale).
    • Use the Google Cloud Shell to explore collections of files and buckets within Google Cloud Storage. Copy, sync, and move data to and from Vertex AI for analysis.
    • Practice by implementing and building upon steps described in one of the quick start guides for Google Cloud Public Datasets.
    • Write your own tutorial notebook explaining a use case in which you are interested.
  • Explore and test Google Cloud Machine learning APIs.
    • Explore using Google Cloud Vision API to compare with the state of the art in computer vision.
    • Explore using Google Cloud Natural Language API to obtain valuable insights from text within documents.
    • Test and analyze the behavior of these machine learning services on your own data using Vertex AI.
    • Write your own analysis notebook. Explain your unique insights into the performance of the ML services and demonstrate this via testing on your own data.

Prerequisites

A background in data science or statistical programming (any language).

Outline

Expand All | Collapse All

Notebook Computing
  • Project Jupyter
  • Data science environments
  • Managed notebook services
  • Google Cloud Vertex AI
Cloud Concepts
  • Definition of a web service
  • Cloud providers
  • Six advantages of cloud computing
  • Different types of cloud computing models (e.g. IAAS, PAAS, SAAS)
  • 5 Principles of cloud computing
  • A new computing paradigm
JupyterLab Interface
  • Jupyter notebook format
  • JupyterLab notebook model
  • Kernels
  • Instances
  • GitHub integration
  • Cloning repositories
Google Cloud Security and Billing
  • Shared responsibility model
  • Google Cloud IAM
  • IAM users, groups, policies, and roles
  • Google Cloud pricing model
  • Securing a new account
  • Google Cloud Console
  • Google Cloud Billing and Cost Explorer
  • Setup a Google Cloud budget alert
  • Google Cloud Shell
Cloud Prerequisites
  • Common Linux distributions on Google Cloud
  • YUM and APT
  • Basic commands such as ls, cp and chmod
  • JSON
  • RESTful APIs
Google Cloud Services
  • Main service categories and core services
  • Regional and Zonal services
  • Services with no charge
  • Google Cloud APIs
  • Google Cloud CLI
  • Google Cloud Python SDK
Google Cloud Storage (gs)
  • Block storage versus object storage
  • Google Storage overview
  • Google Storage classes
  • IAM policies
  • Bucket URLs (two styles)
  • Three common use cases
  • Google Storage pricing
  • gsutil commands for storage
  • Python client library for Google Storage
  • Google Public Datasets
Google Cloud Machine Learning APIs
  • Google Cloud Vision (computer vision service)
  • Google Cloud Natural Language (NLP service)
  • Google Cloud Translate
  • Google Cloud Speech-to-Text
  • Google Cloud Text-to-Speech
Google Cloud Virtual Machines (VMs)
  • Example data science use cases
  • VMs overview
  • Google Cloud Machine Images
  • Instance types
  • Startup scripts on VMs
  • Storage options
  • Tagging
  • Security group settings
  • VM pricing
  • Four pillars of cost optimization
Google Cloud Container Registry
  • Container basics
  • What is Docker
  • JupyterLab on Google Cloud VMs via Docker
  • Google Cloud Container Registry overview
  • Pre-built Docker images for deep learning
Cloud Functions
  • Serverless Google Cloud services
  • Data science use cases of Cloud Functions
  • Event sources
  • Cloud Functions configuration
  • Cloud Functions limits

Training Materials

All GCP for Data Science training students will receive courseware covering the topics in their class.

Software Requirements

A modern web browser and an Internet connection.



Learn faster

Our live, instructor-led lectures are far more effective than pre-recorded classes

Satisfaction guarantee

If your team is not 100% satisfied with your training, we do what's necessary to make it right

Learn online from anywhere

Whether you are at home or in the office, we make learning interactive and engaging

Multiple Payment Options

We accept check, ACH/EFT, major credit cards, and most purchase orders



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