Gain insights into industry-level machine learning by applying advanced TensorFlow and Python techniques. Explore efficient methods for creating scalable models, from data pipelines to deployment and inference.

AML-DLI.tar.gz

case-study

In this course, you'll level up your skills learned in the Industry Case Study and Machine Learning for Software Engineers. You'll take the modeling and data pipeline concepts and apply them to production-level classification and regression models for industry deployment, while continuing to practice the most efficient techniques for building scalable machine learning models. After this course, you will be able to complete industry-level machine learning projects, from data pipeline creation to model deployment and inference.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Deep Learning for Industry

# Chapter Goals:
- Learn how to write serialized protocol buffers to TFRecords files
- Implement a function that writes a list of feature data to a TFRecords file

# A. Serialization
After creating a `tf.train.Example` protocol buffer, we normally store it in a file. To do this, we first have to *serialize* the object, i.e. convert it to a byte string which can be written to a file. The way we serialize a `tf.train.Example` object is through its `SerializeToString` method.

Learn how protocol buffers are stored in TFRecords files.

What you'll learn in this course

Data Pipeline

Model Execution

TFRecords

Chapter Goals:

A. Serialization