Data Pipelines with TensorFlow Data Services (Coursera)

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this third course, you’ll use a suite of tools in TensorFlow to more effectively leverage data and train your model. You’ll learn how to leverage built-in datasets with just a few lines of code, use APIs to control how you split your data, and process all types of unstructured data.

This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Course 3 of 4 in the TensorFlow: Data and Deployment Specialization.

WHAT YOU WILL LEARN

– Leverage built-in datasets with just a few lines of code

– Use APIs to control how you split your data

– Process all types of unstructured data

Syllabus

WEEK 1

Data Pipelines with TensorFlow Data Services

You’ll learn about the types of data that you would normally come across when doing machine learning.

WEEK 2

Exporting your data into the training pipeline

This week you’re going to start looking at the code for using the data with input pipelines!

WEEK 3

Performance

How you load your data into your model can have a huge impact on how efficiently the model trains. You’ll learn how to handle your data input to avoid bottlenecks, race conditions and more!

WEEK 4

Publishing your datasets

This week let’s learn about how you can share your data with the world in a way that’s easy for others to consume!