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 what an Example spec is and what it is used for
- Understand the two different feature configuration classes in TensorFlow
- Implement a function that creates an Example spec from a feature configuration

# A. Example spec
When we read from a TFRecords file for our input pipeline, we get back the serialized versions of the protocol buffers. What we really want, though, are easily accessible data values for each feature from the original protocol buffer. In order to achieve this, we need an *Example spec*.

An Example spec is a dictionary that maps the feature names from the original `tf.train.Example` protocol buffer to particular configuration classes for each feature. Specifically, a feature is in exactly one of two configuration classes: [`VarLenFeature`](https://www.tensorflow.org/api_docs/python/tf/VarLenFeature) or [`FixedLenFeature`](https://www.tensorflow.org/api_docs/python/tf/FixedLenFeature).

# B. `VarLenFeature` class

A `VarLenFeature` specifies a feature whose data value list has variable length, meaning each protocol buffer can have a different number of values for the feature.

Understand how data features are represented in TensorFlow.

What you'll learn in this course

Data Pipeline

Model Execution

Features

Chapter Goals:

A. Example spec

B. `VarLenFeature` class

Features

Chapter Goals:

A. Example spec

B. VarLenFeature class

B. `VarLenFeature` class