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 for training a regression model

# A. Training
The `Estimator` object provides a function called [`train`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#train), which takes care of all the necessary model training tasks. This includes running the training operation, saving and restoring checkpoints, and initializing variables.

The `train` function takes an input data function as the required argument. The input data function must take in no arguments and return a dataset for the input data and labels. In order to create a function that returns the input dataset without taking in any arguments, we usually use a lambda wrapper around our main dataset function.

Use the Estimator API to train a regression model.

What you'll learn in this course

Data Pipeline

Model Execution

Estimator Train

Chapter Goals:

A. Training