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

Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

Data Science

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Oct 19th 2026
5-12 Weeks
Python Machine Learning Clustering
Details
Machine Learning Models in Science (Coursera) Coursera
LearnQuest

Machine Learning Models in Science (Coursera)

Data Science

This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering.

Oct 19th 2026
4 Weeks
Algorithms Machine Learning Neural Networks
Details
Experimental Methods in Systems Biology (Coursera) Coursera
Icahn School of Medicine at Mount Sinai

Experimental Methods in Systems Biology (Coursera)

Sci: Biology & Life Sciences

Learn about the technologies underlying experimentation used in systems biology, with particular focus on RNA sequencing, mass spec-based proteomics, flow/mass cytometry and live-cell imaging. A key driver of the systems biology field is the technology allowing us to delve deeper and wider into how cells respond to experimental perturbations. This in turns allows us to build more detailed quantitative models of cellular function, which can give important insight into applications ranging from biotechnology to human disease. This course gives a broad overview of a variety of current experimental techniques used in modern systems biology, with focus on obtaining the quantitative data needed for computational modeling purposes in downstream analyses.

Oct 19th 2026
5-12 Weeks
Biology Cell Systems Biology
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.

Oct 19th 2026
4 Weeks
Python Machine Learning Asset Management
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.

Oct 19th 2026
4 Weeks
Databases Data Management Data Modeling
Details
Deep Learning for Business (Coursera) Coursera
Yonsei University

Deep Learning for Business (Coursera)

Management & Leadership

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Oct 19th 2026
5-12 Weeks
Business ML Artificial Intelligence
Details
Custom Models, Layers, and Loss Functions with TensorFlow (Coursera) Coursera
DeepLearning.AI

Custom Models, Layers, and Loss Functions with TensorFlow (Coursera)

Data Science

In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network; • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data; • Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions; • Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class.

Oct 19th 2026
5-12 Weeks
Machine Learning Neural Networks Players
Details
Build a Data Warehouse Using BigQuery (Coursera) Coursera
Starweaver

Build a Data Warehouse Using BigQuery (Coursera)

Statistics & Data Analysis Data Science

Unlock the power of Google BigQuery as you embark on a journey to become proficient in data warehouse building and advanced querying. In this comprehensive course, you'll learn to harness the capabilities of BigQuery, from setting up and accessing the platform to creating data warehouses using both the user interface and Python. Through hands-on lessons and practical applications, you'll develop the fundamental skills needed to manage, query, and optimize your data in this powerful cloud-based platform.

Oct 26th 2026
1 Week
SQL Data Warehousing Data Warehouse
Details