Compute on Google Cloud Platform

1

Introducci贸n al curso de Google Cloud for Developer Community

2

Lectura: introducci贸n de instalaci贸n

3

Tutorial de Qwiklabs

4

C贸mputo en la nube de Google

5

Opciones de c贸mputo en la nube

6

M谩quinas virtuales a profundidad

7

Tutorial para instalar Qwiklabs

8

Demo: m谩quinas virtuales a profundidad

9

C贸mputo sin administraci贸n con plataformas como servicio

10

Demo: c贸mputo sin administraci贸n

11

Lectura: 驴qu茅 son los contenedores?

12

C贸mputo contenerizado con App Engine Flex

13

C贸mputo contenerizado con Cloud Run

14

Funciones serverless

Continuous Integration, Continuous Delivery

15

CI/CD en Google Cloud Platform

16

Estrategias de Despliegue

17

Repositorios de c贸digo

18

Construcci贸n y despliegue de artefactos

19

Infraestructura como c贸digo

20

Despliegue en Servicios Serverless

Google Kubernetes Engine

21

Kubernetes Overview

22

Demo Kubernetes

23

Planeaci贸n de tu despliegue

24

Anthos

25

Cloud Run for Anthos

26

Demo Cloud Run for Anthos

27

Anthos Service Mesh

28

Site Reliability Engineering con Anthos

Streaming Data Analytics

29

Integraci贸n de datos e ingesta de datos totalmente administrada sobre GCP

30

Demo: ingesta de datos

31

Ingesta de datos confiable en streaming sobre GCP

32

Demo: ingesta de datos confiable

33

Demo: configuraci贸n de Apache Kafka

34

Visualizaci贸n de mensajes de una base de datos relacional en Google Cloud

35

Data Warehouse: el modelo tradicional para construir un repositorio de datos empresarial

36

Data Lakehouse: el nuevo y moderno enfoque para construir un repositorio de datos empresarial

37

El portafolio de gesti贸n de datos en Google Cloud

38

Desglose del portafolio de gesti贸n de datos (Bases de datos) en Google Cloud

39

Gobierno de datos de punta a punta para garantizar la seguridad en tu Data Lake

40

Gobierno de datos: calidad y monitoreo

Machine Learning

41

驴Qu茅 es ML y AI?

42

Plataforma de AI en GCP

43

Auto ML con datos estructurados

44

Demo Auto ML con datos estructurados

45

Predicci贸n de tarifas usando AI notebooks

46

Demo predicci贸n de tarifas usando AI notebooks

47

TensorFlow Extended

Sesiones en vivo

48

Sesi贸n en vivo con Pablo P茅rez Villanueva

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Resources

What is AutoML and how does it benefit structured data?

AutoML, an advanced machine learning technology, allows companies to implement models without the need to be Machine Learning experts. It lies between pre-built solutions and fully customized development. AutoML makes it easy for downstream users to tackle artificial intelligence and machine learning problems quickly, saving them time and technical effort.

The process simplifies complex tasks by defining data schemas, labeling information and using preconfigured models. AutoML solutions, such as AutoML Vision and AutoML Tables, are ideal for common structured data in business scenarios, generating efficient and personalized predictions.

How does AutoML integrate with Google Cloud?

Google Cloud offers AutoML as a solution to extend technical capabilities with pre-built models that can be customized. This is where Cloud AutoML comes in, providing the flexibility to parameterize models according to business specificities and input data without building a model from scratch.

What are the key steps in AutoML?

  1. Define data and attribute schemas: This includes classifying input data (floats, dates, strings) and setting labels for supervised learning.

  2. Attribute analysis: Identifying which attributes have the greatest impact on predictions is crucial. Domain experts must determine which features in the dataset are the most relevant.

  3. Feature engineering and selection: AutoML automatically selects models and parameters based on the data provided. Users define the initial parameters and AutoML adjusts the hyperparameters automatically.

  4. Model training and evaluation: It is possible to evaluate the behavior of the algorithm by testing and dynamically adjust the model according to specific business needs.

  5. Deployment: Production-ready, the model can be deployed via APIs, scaled on demand and used in a variety of contexts, from mobile applications to cloud infrastructures.

What are the practical applications of AutoML for structured data?

AutoML Tables

AutoML Tables focuses on structured data, representing numerous use cases, such as sales forecasting, risk assessment or weather predictions in IoT systems. AutoML enables an optimized learning and deployment process, lending itself to scenarios that do not require intensive computational investment.

AutoML Vision and other applications

In addition to tabular data, AutoML extends its functionality to images, videos and natural language. For example, technologies behind services such as Google Photos and Google Translate, initially developed in-house, are now available for customization in AutoML. AutoML Vision allows training customized visual models, leveraging advanced computer vision algorithms.

Practical example with Google Analytics

Suppose you have Google Analytics data: using AutoML Tables, you can identify buying patterns based on parameters such as session ID, geography or domain. This capability allows you to make accurate predictions and optimize business strategies from well-categorized historical data.

How does AutoML optimize resource usage?

AutoML uses automatic feature engineering to automatically recognize different types of variables, identifying possible imbalances or correlated labels that affect model performance. Its ability to handle missing data or outliers is crucial for generating robust models.

The underlying algorithms, such as linear regressions, simple neural networks or decision trees, are implemented and selected according to the data, resulting in a robust and scalable solution.

In short, AutoML on Google Cloud democratizes access to machine learning, turning complex challenges into accessible solutions for companies without the need for advanced technical expertise. This tool, through its structured and user-friendly approach, achieves cutting-edge results in different application areas.

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Nota:

Noten que el tipo de algoritmos que corre auto ML son algorirmos supervisados, porque le decimos la tabla objetivo (los resultados que debe obtener).

Wow esto si es ML made easy!