Ementorhub

What you'll 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

Skills you'll gain

Performance Tuning

Data Validation

Feature Engineering

Data Sharing

Data Transformation

Data Management

Tensorflow

Data Import/Export

Data Pipelines

Data Processing

MLOps (Machine Learning Operations)

Extract, Transform, Load

There are 4 modules in this course

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.

Splits and Slices API for Datasets in TF

Exporting Your Data into the Training Pipeline

Performance