Smart Analytics, Machine Learning, and AI on Google Cloud (SAMLAI) – Perfil

Esquema Detallado del Curso

Module 1 - Introduction to Analytics and AI

Topics:

  • What is AI?
  • From ad hoc data analysis to data-driven decisions
  • Options for ML models on Google Cloud

Objectives:

  • Describe the relationship between ML, AI, and deep learning
  • Identify ML options on Google Cloud

Module 2 - Prebuilt ML Model APIs for Unstructured Data

Topics:

  • The difficulties of unstructured data
  • ML APIs for enriching data

Objectives:

  • Discuss challenges when working with unstructured data
  • Identify ready-to-use ML API’s for unstructured data

Module 3 - Big Data Analytics with Notebooks

Topics:

  • Defining notebooks
  • BigQuery magic and ties to Pandas

Objectives:

  • Introduce notebooks as a tool for prototyping ML solutions.
  • Execute BigQuery commands from notebooks.

Module 4 - Production ML Pipelines

Topics:

  • Ways to do ML on Google Cloud
  • Vertex AI Pipelines
  • TensorFlow Hub

Objectives:

  • Describe options available for building custom ML models.
  • Describe the use of tools like Vertex AI and TensorFlow Hub.

Module 5 - Custom Model Building with SQL in BigQuery ML

Topics:

  • BigQuery ML for quick model building
  • Supported models

Objectives:

  • Create ML models by using SQL syntax in BigQuery.
  • Demonstrate building different kinds of ML models by using BigQuery ML.

Module 6 - Custom Model Building with AutoML

Topics:

  • Why use AutoML?
  • AutoML Vision
  • AutoML NLP
  • AutoML Tables

Objectives:

  • Explore various AutoML products used in machine learning.
  • Identify ready-to-use ML API’s for unstructured data.