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 save a model for inference

# A. Deploying a model
As mentioned in chapter 4, a saved model checkpoint consists of three files: `.data`, `.index`, and `.meta`. Since the `.meta` file contains the entire computation graph structure, which includes all the data in the training dataset, it can get quite large. The large file size becomes an issue when deploying an *inference* model.

An inference model is a fully trained and evaluated model used to make predictions on real-time data. When we deploy an inference model for production, we don't usually deploy the code used to build the model, either for proprietary reasons or because there are too many auxiliary code files. When we don't have the code that sets up the inference graph, we need a separate file that specifies the computation graph's structure.

Save a condensed model to be used for inference on real-time data.

What you'll learn in this course

Data Pipeline

Model Execution

Save For Inference

Chapter Goals:

A. Deploying a model