Thursday, January 20th

Cloud Functions within Google Cloud

Join Google Cloud Developer Programs Engineer Charlie Engelke and learn how to use Cloud Functions. You will:

• Create a small serverless application demonstrating a simple microservices architecture.
• Create a Pub/Sub topic and subscription
• Create Cloud Functions to process published messages in JavaScript and Python, using the Cloud Console UI, and using gcloud command line interface in Google Cloud Shell.
• Create a CloudSQL database to store the status of the processed widgets.
• Use the Runtime Configurator to store and share credentials with Cloud Functions.
• Create a Cloud Scheduler Job to call an HTTP end point.
• How to filter and export Cloud Logging messages to Cloud Pub/Sub
• How to trigger Cloud Functions from Pub/Sub
• How to do write a Cloud Function to do simple processing
• How to update a VM's metadata

Thursday, January 27th

Intro to ML: Language Processing

Join Google Cloud Technical Account Manager Abhishek Kanal and learn more about Machine Learning and Google Cloud.

It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.

Thursday, February 3rd

Create ML Models with BigQuery ML

Join Google Customer Engineer Sagar Kewalramani for a hands-on workshop showcasing how to Create ML Models with BigQuery ML.

BigQuery Machine Learning (BQML, product in beta) enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratise machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement. There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this workshop you will use this data to create a model that predicts whether a visitor will make a transaction.