Course details

Enterprise Services: Machine Learning Implementation on Google Cloud Platform

Enterprise Services: Machine Learning Implementation on Google Cloud Platform


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This course explores the GCP (Google Cloud Platform) machine learning (ML) tools, services, and capabilities, and different stages in the Google Cloud Platform machine learning workflow. This 14-video course demonstrates a high-level overview of different stages in Google Cloud Platform machine learning workflow. You will examine the features of BigQuery, and how to use Big Query ML to create and evaluate a binary logistic regression model using Big Query ML statement. Next, learners will observe how to use the Google AI Platform and Google Cloud AutoML components and features used for training, evaluating, and deploying ML models. You will learn to train models by using the built-in linear learner algorithm, submit jobs with GCloud and Console, create and evaluate binary logistic regression models, and set up and work with Cloud Datalab. You will learn to use AutoML Tables to work with data sets, to train machine learning models for predictions. Finally, you will work with Google Cloud AutoML Natural Language to create custom ML models for content category classification.



Expected Duration (hours)
1.0

Lesson Objectives

Enterprise Services: Machine Learning Implementation on Google Cloud Platform

  • discover the key concepts covered in this course
  • describe GCP machine learning tools, services, and capabilities
  • describe the Google Cloud Platform machine learning implementation approach and the different stages in Google Cloud Platform machine learning workflow
  • train models using the built-in linear learner algorithm and submit jobs with GCloud and Console
  • recall the essential features of BigQuery along with the capabilities of BigQuery ML
  • create and evaluate binary logistic regression models using BigQuery ML statements
  • recognize the challenges associated with modern machine learning workflows and how you can leverage the serverless approach to eliminate those challenges
  • set up and work with Cloud Datalab
  • recognize Google AI Platform components and features that can be used to build machine learning workflows and train machine learning models at scale
  • recall Google Cloud AutoML features and how it can be used to train, evaluate, and deploy machine learning models
  • use AutoML Tables to work with datasets needed to train and use machine learning models
  • work with AutoML Tables to train machine learning models using imported datasets and use the trained models for predictions
  • work with Google Cloud AutoML Natural Language to create custom machine learning models for content category classification
  • summarize the key concepts covered in this course
  • Course Number:
    it_mlmdesdj_03_enus

    Expertise Level
    Intermediate