This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
Build, Train and Deploy ML Models with Keras on Google Cloud
Build, Train and Deploy ML Models with Keras on Google Cloud
This course is part of multiple programs.
Instructor: Google Cloud Training
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55,062 already enrolled
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
- Model Evaluation
- Machine Learning
- Feature Engineering
- Keras (Neural Network Library)
- Google Cloud Platform
- MLOps (Machine Learning Operations)
- Data Transformation
- Tensorflow
- Model Deployment
- Data Preprocessing
- Cloud Deployment
- Python Programming
- Data Pipelines
- Deep Learning
- Artificial Neural Networks
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Assessments
4 assignments
Taught in English
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There are 6 modules in this course
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
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
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
In this module, we describe how to train TensorFlow models at scale using Vertex AI.
What's included
3 videos1 reading1 assignment1 app item
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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Showing 3 of 2809
YG
Reviewed on Apr 4, 2019
The procedure to connect to the cloud datalab was time consuming to do it every time.Suggestion : More topics in Core Tensorflow could be added. I enjoyed the course!
JZ
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.
TB
Reviewed on Oct 6, 2018
Great course as an introduction to TF, however, the labs are not as in depth as I'd have liked. Nonetheless, the course is well executed by the presenters.
