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1
Introduction
2
Meet Matt Feigal
3
What is Tulip Translator
4
Machine Learning
5
Gathering Data
6
Training the Model
7
Evaluating the Model
8
Testing the Model
9
Future of Google Tulip
10
Cristian Hees
11
The most important part
12
Im a developer
13
Cloudrun
14
Docker
15
Container Registry
16
Build Trigger
17
The Real World
18
Deployment
19
How does this work
20
Dialogue Flow
21
Demo
22
How we built
23
Creating a request
24
Wrapping up
25
Github
Description:
Explore how Google Tulip was built using serverless technology and machine learning in this conference talk from GOTO Amsterdam 2019. Discover the process of composing an application with multiple serverless components and learn how to train an ML model with minimal data for practical application. Gain insights into the Tulip Translator project, including data gathering, model training, evaluation, and testing. Delve into the technical aspects of development using Cloudrun, Docker, Container Registry, and Build Trigger. Understand the implementation of Dialogue Flow and see a live demo of the application. Follow along as the speakers discuss the creation process, deployment strategies, and real-world applications. Access the complete talk, including slides and additional resources, to enhance your understanding of serverless technology and machine learning in innovative projects.

How We Built Google Tulip by Using Serverless Technology and Machine Learning

GOTO Conferences
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