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

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
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.

Oct 19th 2026
4 Weeks
Software Machine Learning Computer Science
Details
Data Engineering Capstone Project (Coursera) Coursera
IBM

Data Engineering Capstone Project (Coursera)

CS: Information & Technology

In this course you will apply a variety of data engineering skills and techniques you have learned as part of the previous courses in the IBM Data Engineering Professional Certificate. You will assume the role of a Junior Data Engineer who has recently joined the organization and be presented with a real-world use case that requires a data engineering solution.

Oct 19th 2026
5-12 Weeks
Python NoSQL Spark
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
Practical Python for AI Coding 2 (Coursera) Coursera
Korea Advanced Institute of Science and Technology - KAIST

Practical Python for AI Coding 2 (Coursera)

CS: Software Engineering CS: Programming

This course is for a complete novice of Python coding, so no prior knowledge or experience in software coding is required. This course selects, introduces and explains Python syntaxes, functions and libraries that were frequently used in AI coding. In addition, this course introduces vital syntaxes, and functions often used in AI coding and explains the complementary relationship among NumPy, Pandas and TensorFlow, so this course is helpful for even seasoned python users.

Oct 26th 2026
5-12 Weeks
Python Object-Oriented Programming Data Visualization
Details
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera) Coursera
Icahn School of Medicine at Mount Sinai

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera)

Health & Society Science

In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Oct 19th 2026
5-12 Weeks
Network Analysis Big Data
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
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.

Oct 19th 2026
4 Weeks
TensorFlow Deep Learning Autoencoders
Details
ETL and Data Pipelines with Shell, Airflow and Kafka (Coursera) Coursera
IBM

ETL and Data Pipelines with Shell, Airflow and Kafka (Coursera)

CS: Information & Technology

After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.

Oct 19th 2026
5-12 Weeks
ETL Kafka Data Pipelines
Details