Smart Analytics, Machine Learning, and AI on Google Cloud (SAMLAI) - Online Training

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 APIs 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 APIs for unstructured data.

Lernziele

• Differentiate between ML, AI and deep learning.
• Discuss the use of ML APIs on unstructured data.
• Execute BigQuery commands from notebooks.
• Create ML models by using SQL syntax in BigQuery.
• Create ML models without coding by using AutoML

Zielgruppen

Data Engineers