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LinkedIn Learning

Google Cloud Platform for Machine Learning Essential Training

via LinkedIn Learning

Overview

Learn how to design machine learning solutions with Google Cloud Platform. Review services such as AutoML, CloudML Engine, and the GCP machine learning APIs.

Machine learning can make your applications faster and more intelligent. You can analyze customer data such as voice and text input, images, and video, and take action without human intervention. Google Cloud Platform (GCP) offers a competitive set of machine learning services for nearly every type of architecture, including serverless computing, containers, and virtual machines. Learn how to design your own machine learning solutions using GCP, in this introductory course with instructor Lynn Langit. Lynn shows how to identify your requirements and map them to services such as the GCP machine learning APIs—Cloud Vision, Cloud Speech-to-Text, Cloud Video Intelligence, and more—and GCP AutoML, which puts the same APIs behind an easy-to-use interface. Then get an overview of the custom ML models and deep neural networks that are possible in Google Cloud ML Engine. Finally, review five different practical examples of GCP machine learning, including a chat bot, an image search application, and an on-device Internet of Things application.

Syllabus

Introduction
  • Build complete solutions with machine learning services
  • What you should know
  • About using cloud services
1. Machine Learning on Google Cloud Platform
  • Business scenarios for machine learning
  • Which algorithm should you use?
  • GCP AI servers vs. platforms
  • Enable GCP ML APIs
  • Data preparation with Cloud Dataflow and Cloud Dataprep
  • An ML notebook in action: Colaboratory
  • An ML notebook in action: Set up Cloud Datalab
  • An ML notebook in action: Use Cloud Datalab
2. Machine Learning API Services
  • Overview of GCP ML APIs
  • Predict via the Cloud Vision API for images
  • Predict via the Cloud Video Intelligence API for video
  • Predict via the Natural Language API for NLP
  • Predict via the Text-to-Speech API
  • Predict via the Speech-to-Text API
  • Predict via the Cloud Translation API
  • Predict via BigQuery ML
3. Machine Learning with AutoML
  • Understand Cloud AutoML services
  • Understand AutoML Vision
  • Prepare data and labels for AutoML Vision
  • Train model for AutoML Vision
  • Evaluate model with AutoML Vision
  • Predict using a trained AutoML Vision model
4. Advanced Machine Learning
  • Why build custom ML models?
  • Using containers to host ML models
  • Use Cloud ML Engine
  • Evaluate Cloud ML Engine output
  • Scale custom ML models
  • Understanding deep learning
  • Work with TensorBoard
  • Work with Keras for TensorFlow
  • GPUs and TPUs for TensorFlow
  • TensorFlow for JavaScript and mobile
5. Machine Learning Architectures
  • Chatbot with ML
  • Image search with Cloud Vision and Cloud ML
  • GCP serverless machine learning architecture
  • GCP machine learning with structured data
  • GCP ML service for IoT apps
Conclusion
  • Next steps

Taught by

Lynn Langit

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