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:
- Understand how the checkpoint directory is structured
- Learn how to restore model parameters from a checkpoint

# A. Checkpoint directory
After running training with a checkpoint directory, it will contain several files. An example checkpoint directory, named `my_model`, is shown below.

The `.pbtxt` file represents the entire computation graph stored in human readable text format. The `.tfevents` file is the events file for TensorBoard (note that the longer file suffix, which contains the local machine's ID, is omitted).

The actual saved model state at a particular training step consists of the following three files:

- `.data`: One or more files containing the values for the model's parameters. Larger models may require more `.data` files.
- `.index`: Metadata descriptions for how to find a particular tensor in the `.data` file(s).
- `.meta`: Represents the non-human readable saved graph structure. This file can be used to restore the computation graph.

The `checkpoint` file lists which checkpoint to use when restoring parameters, as well as all the possible checkpoints available.

Understand how checkpoints are used to save and load TensorFlow models.

What you'll learn in this course

Data Pipeline

Model Execution

Checkpoint

Chapter Goals:

A. Checkpoint directory