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Introduction to AI on Google Cloud

A beginner’s guide to starting AI and machine learning with Google Cloud

Harshit Tyagi

Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. This training session intends to teach about Google Cloud Platform (GCP) and how can you start off your journey in the AI and data space. GCP provides user-friendly services in these areas. We’ll learn from live code labs and cover the introductory-level quest encapsulating Introduction to SQL for Big Query, BigQuery quickstart guide, and Cloud ML Engine.

What you'll learn-and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • Fundamental SQL clauses
  • How to run structured queries on Bigquery
  • How to query public tables and train a Machine Learning (ML) model

And you’ll be able to:

  • Store and query massive datasets using Bigquery
  • Train and deploy a tensorflow model to Cloud ML engine

This training course is for you because...

  • You are a programmer or an aspiring data engineer.
  • You are a beginner in the field of Data/ML/AI with some familiarity with elementary mathematics and programming.

Prerequisites

  • Familiarity with programming and basic SQL
  • Understanding of basic mathematics

Recommended preparation:

Recommended follow-up:

About your instructor

  • Harshit Tyagi is a Full Stack Developer and Data Engineer at Elucidata, a Cambridge based Biotech company. He develops algorithms for research scientists at the world’s best medical schools like Yale, UCLA, and MIT. Before Elucidata, he was working as a Systems Development Engineer at an Investment Management firm called Tradelogic where he designed a framework to analyze financial news from all prominent sources to produce accurate trading signals. He is a Python evangelist and loves to contribute to tech communities like Google Developers Groups, Python Delhi User Groups, and other E-learning platforms. With the skills acquired over years and being a mentor and reviewer for more than 3 years in the E-learning era, it’d be great to share the enterprise-grade practices to produce more skillful data scientists and quantitative traders.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction to SQL for Cloud SQL and BigQuery (60 minutes)

  • Presentation: What is Data and what’s the role of a Data scientist at a data-driven organization?
  • BigQuery Lab walkthrough: Get insights from structured datasets using SQL. We’ll learn fundamental SQL and querying keywords and run them in BigQuery console on a public dataset.
  • Presentation: Learn how to export subsets of datasets into CSV files and upload them to CloudSQL to create and manage databases and tables.
  • Exercise and Q&A: Exercise questions on what we learned.
  • Break (5 mins)

BigQuery: Quickstart - Console (45 minutes)

  • Presentation: Introduction to BigQuery console.
  • Console lab walkthrough: Learn how to use Web UI to query public tables and load sample data into BigQuery.
  • Q&A
  • Break (5 mins)

Training a tensorflow model on Cloud ML Engine: Quickstart (60 minutes)

  • Presentation: Quick intro to tensorflow Machine learning library.
  • Codelab walkthrough: Learn to train Tensorflow model both locally and on Cloud ML engine.
  • Deployment: Learn to deploy the model you’ve built for serving prediction. We’ll try to predict the income category of a person using the US census income dataset.
  • Q&A

Take-home exercise: Create and deploy your own tensorflow model on Cloud ML Engine