This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
Offered By
About this Course
What you will learn
Create TensorFlow and Keras machine learning models and understand their key components
Use the tf.data library to manipulate data and 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 will gain
- Machine Learning
- Python Programming
- Build Input Data Pipeline
- Tensorflow
- keras
Offered by

Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Introduction to the Course
This module provides an overview of the course and its objectives.
Introduction to the TensorFlow ecosystem
This module introduces the TensorFlow framework and previews its main components as well as the overall API hierarchy.
Design and Build an Input Data Pipeline
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.
Building Neural Networks with the TensorFlow and Keras API
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.
Training at Scale with Vertex AI
In this module, we describe how to train TensorFlow models at scale using Vertex AI.
Summary
This module is a summary of the TensorFlow on Google Cloud course.
Reviews
- 5 stars61.82%
- 4 stars25.10%
- 3 stars8.97%
- 2 stars2.56%
- 1 star1.52%
TOP REVIEWS FROM TENSORFLOW ON GOOGLE CLOUD
The tools and methods presented were great. The instructors were also fantastic. However the coding exercises were lacking in guidance even though the complete solution is given in the video.
pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!
Amazing course! The short length of videos makes it lot easier for students to follow! Google is honestly the best at whatever it does! :)
I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.
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