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 use an `Estimator` object to make regression predictions

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

Like the `train` and `evaluate` functions, `predict` also takes an input data function as its required argument. However, the input data function for `predict` does not need to return any labels, since we don't return the loss when making model predictions. In fact, it is often the case that there are no actual labels for the predicted data observations.

The `predict` function will return a [generator](https://wiki.python.org/moin/Generators) of dictionaries, where each dictionary corresponds to a predicted data observation. The dictionary consists of the output values specified in the model function's prediction `ExampleSpec`.

# Chapter Goals:
- Learn how to use an `Estimator` object to make regression predictions

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

Like the `train` and `evaluate` functions, `predict` also takes an input data function as its required argument. However, the input data function for `predict` does not need to return any labels, since we don't return the loss when making model predictions. In fact, it is often the case that there are no actual labels for the predicted data observations.

The `predict` function will return a [generator](https://wiki.python.org/moin/Generators) of dictionaries, where each dictionary corresponds to a predicted data observation. The dictionary consists of the output values specified in the model function's prediction `ExampleSpec`.

Use the Estimator API to make predictions with the regression model.

What you'll learn in this course

Data Pipeline

Model Execution

Estimator Predict

Chapter Goals:

A. Prediction