Build, Train and Deploy ML Models with Keras on Google Cloud

This course is part of multiple programs.

Instructor: Google Cloud Training

Access provided by Leidos

55,244 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

2,809 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

88%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

2,809 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

88%

Most learners liked this course

What you'll learn

Design and build a TensorFlow input data pipeline.
Use the tf.data library to manipulate data in large datasets.
Use the Keras Sequential and Functional APIs for simple and advanced model creation.
Train, deploy, and productionalize ML models at scale with Vertex AI.

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

4 assignments

Taught in English

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There are 6 modules in this course

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

This module provides an overview of the course and its objectives.

What's included

1 video

This module introduces the TensorFlow framework and previews its main components as well as the overall API hierarchy.

What's included

4 videos1 reading1 assignment

4 videos Total 19 minutes

Introduction to the TensorFlow ecosystem 0 minutes
Introduction to TensorFlow 6 minutes
TensorFlow API Hierarchy 5 minutes
Components of TensorFlow: Tensors and Variables 9 minutes

1 reading Total 10 minutes

Resources: Introduction to the TensorFlow Ecosystem 10 minutes

1 assignment Total 12 minutes

Introduction to the TensorFlow Ecosystem 12 minutes

Data is the a crucial component of a machine learning model. Collecting the right data is not enough. You also need to make sure you put the right processes in place to clean, analyze and transform the data, as needed, so that the model can take the most signal of it as possible. In this module we discuss training on large datasets with tf.data, working with in-memory files, and how to get the data ready for training. Then we discuss embeddings, and end with an overview of scaling data with tf.keras preprocessing layers.

What's included

9 videos1 reading1 assignment2 app items1 plugin

9 videos Total 36 minutes

Introduction 0 minutes
An ML recap 3 minutes
Training on large datasets with tf.data API 4 minutes
Working in-memory and with files 4 minutes
Getting the data ready for model training 6 minutes
Embeddings 9 minutes
Lab intro: TensorFlow Dataset API 0 minutes
Scaling data processing with tf.data and Keras preprocessing layers 10 minutes
Lab intro: Classifying structured data using Keras preprocessing layers 1 minute

1 reading Total 10 minutes

Resources: Design and Build a TensorFlow Input Data Pipeline 10 minutes

1 assignment Total 16 minutes

Design and Build Input Data Pipeline 16 minutes

2 app items Total 240 minutes

Lab: TensorFlow Dataset API 120 minutes
Lab: Classifying structured data using Keras preprocessing layers 120 minutes

1 plugin Total 15 minutes

Accessing and completing labs 15 minutes

In this module, we discuss activation functions and how they are needed to allow deep neural networks to capture nonlinearities of the data. We then provide an overview of Deep Neural Networks using the Keras Sequential and Functional APIs. Next we describe model subclassing, which offers greater flexibility in model building. The module ends with a lesson on regularization.

What's included

10 videos1 reading1 assignment2 app items

10 videos Total 45 minutes

Introduction 1 minute
Activation functions 9 minutes
Training neural networks with TensorFlow 2 and the Keras Sequential API 8 minutes
Serving models in the cloud 3 minutes
Lab intro: Introducing the Keras Sequential API on Vertex AI Platform 1 minute
Training neural networks with TensorFlow 2 and the Keras Functional API 10 minutes
Lab intro: Build a DNN using the Keras Functional API on Vertex AI Platform 0 minutes
Model subclassing 4 minutes
Regularization basics 5 minutes
How can we measure model complexity: L1 vs. L2 Regularization 5 minutes

1 reading Total 10 minutes

Resources: Building Neural Networks with the TensorFlow and Keras API 10 minutes

1 assignment Total 18 minutes

Building Neural Networks in TensorFlow with Keras API 18 minutes

2 app items Total 180 minutes

Lab: Introducing the Keras Sequential API on Vertex AI Platform 60 minutes
Lab: Build a DNN using the Keras Functional API on Vertex AI Platform 120 minutes

In this module, we describe how to train TensorFlow models at scale using Vertex AI.

What's included

3 videos1 reading1 assignment1 app item

3 videos Total 7 minutes

Introduction 0 minutes
Training at scale with Vertex AI 6 minutes
Lab intro: Training at scale with the Vertex AI Training Service 1 minute

1 reading Total 10 minutes

Resources: Training at Scale with Vertex AI 10 minutes

1 assignment Total 10 minutes

Training at Scale with Vertex AI 10 minutes

1 app item Total 120 minutes

Lab: Training at scale with the Vertex AI Training Service 120 minutes

This module is a summary of the Build, Train, and Deploy ML Models with Keras on Google Cloud course.

What's included

4 readings

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Instructor

Instructor ratings

(197 ratings)

Google Cloud Training

Google Cloud

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Google Cloud

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Reviewed on Mar 30, 2020

This needs some updating - looks like the Tensorboard is no longer accessed in the same way as it was when this course was produced. Otherwise great! Good challenges! :/

Reviewed on Aug 10, 2018

Pretty helpful in getting to know the various levels of abstractions of tensorflow API and avoiding various pitfalls while building the Tensorflow model

Reviewed on Nov 3, 2022

Quite a technical course with sophisticated lab sessions, but I got good hands-on experience on building NN models using Keras and TF functional API as well as deploying the model in Vertex AI.

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