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:
- Create an <code>Estimator</code> object for the regression model

# A. Creating an `Estimator`
TensorFlow's [`Estimator`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator) object provides an organized and simple API for model execution. It handles model training, saving and restoring checkpoints, evaluating a model, and making predictions.

To initialize an `Estimator` object, we pass in the model function as a required argument. The model function should follow the same template as `regressor_fn`. The function must return an `ExampleSpec` object, which specifies the model results for training, evaluation, or prediction.

The two main keyword arguments to know are `model_dir` and `params`. The `model_dir` argument represents the directory we save model checkpoints to. The `params` argument represents the values we wish to pass into the model function. The argument should be set to a dictionary, which then corresponds to the model function's `params` argument.

Learn how to use TensorFlow's Estimator API for model development.

What you'll learn in this course

Data Pipeline

Model Execution

Estimator

Chapter Goals:

A.