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Smart Analytics, Machine Learning, and AI on Google Cloud

This course is part of multiple programs. Learn more

Instructor: Google Cloud Training

What you'll learn

Differentiate between ML, AI and deep learning.

Discuss the use of ML API’s on unstructured data.

Execute BigQuery commands from notebooks.

Create ML models by using SQL syntax in BigQuery and without coding using Vertex AI AutoML.

Skills you'll gain

Data Pipelines

Jupyter

Tensorflow

Big Data

Artificial Intelligence and Machine Learning (AI/ML)

MLOps (Machine Learning Operations)

Machine Learning

Unstructured Data

Applied Machine Learning

Natural Language Processing

Google Cloud Platform

There are 8 modules in this course

Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

Introduction to Analytics and AI

Prebuilt ML model APIs for Unstructured Data

Big Data Analytics with Notebooks

Production ML Pipelines

Custom Model building with SQL in BigQuery ML

Custom Model Building with Vertex AI AutoML

Summary

Explore more from Cloud Computing

Introduction to AI and Machine Learning on Google Cloud

Machine Learning on Google Cloud

How Google does Machine Learning

Applying Machine Learning to your Data with Google Cloud