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:
- Use an `Estimator` object to evaluate a regression model

## A. Evaluation
The `Estimator` object provides a function called [`evaluate`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#evaluate), which is used for model evaluation.

Like the `train` function, `evaluate` also takes an input data function as its required argument. The input data function must follow the same format as the one used with `train`, although it can be configured differently (i.e. no shuffling, smaller batch size, etc.). We can also specify the number of steps to run evaluation for using the `steps` keyword argument.

The `evaluate` function will return a dictionary containing the final values for each evaluation metric. Each key is the name of a metric and each value is that metric's value. The dictionary metrics consist of the model loss (corresponding to key `'loss'`), as well as each metric we specified in `eval_metric_ops` when initializing the evaluation `ExampleSpec`. The dictionary will also include the final training iteration of the model, represented by `'global_step'`.

# Chapter Goals:
- Use an `Estimator` object to evaluate a regression model

# A. Evaluation
The `Estimator` object provides a function called [`evaluate`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#evaluate), which is used for model evaluation.

Like the `train` function, `evaluate` also takes an input data function as its required argument. The input data function must follow the same format as the one used with `train`, although it can be configured differently (i.e. no shuffling, smaller batch size, etc.). We can also specify the number of steps to run evaluation for using the `steps` keyword argument.

The `evaluate` function will return a dictionary containing the final values for each evaluation metric. Each key is the name of a metric and each value is that metric's value. The dictionary metrics consist of the model loss (corresponding to key `'loss'`), as well as each metric we specified in `eval_metric_ops` when initializing the evaluation `ExampleSpec`. The dictionary will also include the final training iteration of the model, represented by `'global_step'`.

Use the Estimator API to evaluate the regression model.

What you'll learn in this course

Data Pipeline

Model Execution

Estimator Eval

Chapter Goals:

A. Evaluation