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

  •   Machine Learning
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Jupyter
  •   MLOps (Machine Learning Operations)
  •   Big Data
  •   Tensorflow
  •   Applied Machine Learning
  •   Google Cloud Platform
  •   Natural Language Processing
  •   Data Pipelines
  •   Unstructured Data

    • 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

    Applying Machine Learning to your Data with Google Cloud

    Digital Transformation Using AI/ML with Google Cloud

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