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 restore an inference model and retrieve specific tensors from the computation graph
- Implement a function that makes predictions using a saved inference model

# A. Restoring the model
To restore an inference model, we use the [`tf.compat.v1.saved_model.loader.load`](https://www.tensorflow.org/api_docs/python/tf/saved_model/loader/load) function. This function restores both the inference graph as well as the inference model's parameters.

Since the function's first argument is a `tf.compat.v1.Session` object, it's a good idea to restore the inference model within the scope of a particular `tf.compat.v1.Session`.

import tensorflow as tf

tags = [tf.compat.v1.saved_model.tag_constants.SERVING]
model_dir = 'inference_model'
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess, tags, model_dir)

The second argument for `tf.compat.v1.saved_model.loader.load` is a list of [tag constants](https://www.tensorflow.org/api_docs/python/tf/saved_model/tag_constants). For inference, we use the `SERVING` tag. The function's third argument is the path to the saved inference model's directory.

In the example above, we restored the inference model within the scope of a `tf.compat.v1.Session` object named `sess`. Note that `sess` is initialized with a new, empty computation graph in `tf.Graph()`. This ensures that there are no graph conflicts, similar to `tf.compat.v1.reset_default_graph`, but also that it doesn't erase the graph outside the scope of `sess`.

Restore an inference model and make predictions on an input dataset.

What you'll learn in this course

Data Pipeline

Model Execution

Predictions

Chapter Goals:

A. Restoring the model