I/O Extended 2021 - Pacific Coast

Name: I/O Extended 2021 - Pacific Coast
Start: 2021-07-21T18:00:00-07:00
End: 2021-07-24T20:00:00-07:00

GDG Cloud San Francisco

Come join us for a fun recap of Google I/O extended event and learn what's new with Flutter and Vertex AI.

Jul 22, 2021, 1:00 AM – Jul 25, 2021, 3:00 AM

25

RSVP'd

Key Themes

CloudFlutterMobile

About this event

RSVP => https://gdg.community.dev/e/my7xsn/

Come join us for a fun recap of Google I/O extended event and learn what's new with Flutter and Vertex AI
Organized by: GDG Modesto, GDG Fresno / WTM Fresno, GDG San Francisco, GDG Cloud San Francisco, GDG Los Angeles, GDG Sonoma
Giveaways:
10 Coursera courses
2 digital books from raywenderlich.com
5 books from O'Reilly 
All the attendees will also receive a 30-day trial to O'Reilly learning platform

When

July 22 – 25, 2021
1:00 AM – 3:00 AM UTC

Agenda

Welcome
Google I/O Recap by Jim Hutson
Null Safety by Randal Schwartz	We’ve had Null Safety since the beginning of Dart. Can’t execute any method on null without throwing an error (one error I would be surprised if you’ve never seen). So why is this news? Oh, you mean “compile-time Null Safety by default?” (Also known as "Non-Null By Default", or “NNBD".) That’s relatively new, arriving with Dart beta releases for the past year, but finally landing in stable releases with Dart 2.12.0 (and sometimes mistakenly confused with the release of Flutter 2.0). But what is NNBD? And why is it important to us? What benefits do we get? What are our responsibilities as programmers? And very important (if you have anything written before the start of this year)… “how do I migrate my legacy code to modern Dart to get all of this, and what tooling is provided for that, and what are the concerns?”. Randal will go over history, current status, and migration tools, as well as some quirks that might not seem obvious until you work it out, and how Flutter 2.2 has changed to “NNBD-BD”.
MLOps with Vertex AI by Kaz Sato	The hardest part of ML adoption in enterprises is Productinization. As we see in recent discussions around MLOps, there is a big gap between Data Scientists' PoC code and production ML development and operation with the Ops team. Such as, preparing a manageable ML dev environment, building a scalable ML serving infrastructure, setting up a ML pipeline for continuous training, managing features in a scalable way, and monitoring the model performance in the production. In this session, we will learn how to leverage various Google's MLOps best practices and Vertex AI products to solve these problems.
Closing Remarks & Giveaways

Speakers

Kaz Sato

Google

Developer Advocate

Randal Schwartz

Flutter/Dart GDE

Jim Hutson

IDG Engineering Solutions

Principal Software Engineer

Organizers

Vikram Tiwari

Omni Labs, Inc.

GDG Organizer

See bio

Riya Dashoriya

Quizlet

GDG Organizer

See bio

Contact Us