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 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.
Introduction
Skills You'll Learn
Tensorflow, Data Management, Data Validation, Performance Tuning, MLOps (Machine Learning Operations), Data Pipelines, Data Processing, Feature Engineering, Data Transformation, Data Import/Export, Extract, Transform, Load, Data Sharing
Reviews
4.5 (534 ratings)
- 5 stars68.16%
- 4 stars17.97%
- 3 stars8.05%
- 2 stars3.55%
- 1 star2.24%
SB
May 11, 2020
I was really looking forward to learning efficient data pipelines and that is utterly what I learned here in this course. Best in its class.
SZ
May 20, 2020
I learned a lot from this course about how to optimize TensorFlow data pipelines and how to create public datasets. Thank you! - Steve
From the lesson
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
Taught By
Laurence Moroney
Instructor