Coursera

Data Pipelines with TensorFlow Data Services (Coursera)

Offered by DeepLearning.AI,
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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

In this third course, you will:

  • Perform streamlined ETL tasks using TensorFlow Data Services
  • Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs
  • Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset
  • Optimize data pipelines that become a bottleneck in the training process
  • Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the world

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.

What You Will Learn

  • Perform efficient ETL tasks using Tensorflow Data Services APIs
  • Construct train/validation/test splits of any dataset - either custom or present in TensorFlow Hub Dataset library - using Splits API
  • Use different modules and functions of the TFDS API to prepare your data for training pipelines
  • Identify bottlenecks in your input pipelines and increase your workflow efficiency by input parallelization

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow

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

Syllabus

WEEK 1
Data Pipelines with TensorFlow Data Services
This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs

WEEK 2
Splits and Slices API for Datasets in TF
In this week, you will construct train/validation/test splits of any dataset - either custom or present in TensorFlow hub dataset library - using Splits API

WEEK 3
Exporting Your Data into the Training Pipeline
This week you will extend your knowledge of data pipelines

WEEK 4
Performance
You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Prepare Data for Exploration (Coursera) Coursera
Google

Prepare Data for Exploration (Coursera)

Statistics & Data Analysis Data Science

This is the third course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. As you continue to build on your understanding of the topics from the first two courses, you’ll also be introduced to new topics that will help you gain practical data analytics skills. You’ll learn how to use tools like spreadsheets and SQL to extract and make use of the right data for your objectives and how to organize and protect your data. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Jun 15th 2026
5-12 Weeks
Data Analysis Databases
Details
The Path to Insights: Data Models and Pipelines (Coursera) Coursera
Google

The Path to Insights: Data Models and Pipelines (Coursera)

Statistics & Data Analysis Data Science

This is the second of three courses in the Google Business Intelligence Certificate. In this course, you'll explore data modeling and how databases are designed. Then you’ll learn about extract, transform, load (ETL) processes that extract data from source systems, transform it into formats that enable analysis, and drive business processes and goals.

Jun 15th 2026
4 Weeks
Databases Data Management Data Modeling
Details
Generative Deep Learning with TensorFlow (Coursera) Coursera
DeepLearning.AI

Generative Deep Learning with TensorFlow (Coursera)

Data Science

In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image; b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one; c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images; d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces.

Jun 15th 2026
4 Weeks
TensorFlow Deep Learning Autoencoders
Details
Python and Machine-Learning for Asset Management with Alternative Data Sets (Coursera) Coursera
EDHEC Business School

Python and Machine-Learning for Asset Management with Alternative Data Sets (Coursera)

Economics & Finance Business

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications.

Jun 15th 2026
4 Weeks
Python Machine Learning Asset Management
Details
Advanced Deployment Scenarios with TensorFlow (Coursera) Coursera
DeepLearning.AI

Advanced Deployment Scenarios with TensorFlow (Coursera)

Computer Science

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 final course, you’ll explore four different scenarios you’ll encounter when deploying models.

Jun 15th 2026
4 Weeks
Software Machine Learning Computer Science
Details
Mathematics for Machine Learning: PCA (Coursera) Coursera
Imperial College London

Mathematics for Machine Learning: PCA (Coursera)

Data Science

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

Jun 15th 2026
4 Weeks
Statistics Analysis Machine Learning
Details
Follow a Machine Learning Workflow (Coursera) Coursera
CertNexus

Follow a Machine Learning Workflow (Coursera)

Data Science

Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution. This second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment.

Jun 15th 2026
5-12 Weeks
ML Artificial Intelligence Machine Learning
Details
Customising your models with TensorFlow 2 (Coursera) Coursera
Imperial College London

Customising your models with TensorFlow 2 (Coursera)

Data Science

Welcome to this course on Customising your models with TensorFlow 2! In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.

Jun 8th 2026
5-12 Weeks
Machine Learning Modeling APIs
Details
Getting started with TensorFlow 2 (Coursera) Coursera
Imperial College London

Getting started with TensorFlow 2 (Coursera)

Data Science

Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

Jun 8th 2026
5-12 Weeks
Machine Learning Data Science APIs
Details