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 create a dataset in TensorFlow
- Implement a function that creates a dataset from NumPy data

# A. Input pipeline
In TensorFlow, the input pipeline for executing a machine learning model is represented by the [`Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) class (which we'll refer to as simply a dataset). A dataset can be created for a variety of input values, from NumPy arrays to protocol buffers. The most basic way to create a dataset is with the [`tf.data.Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) function.

import numpy as np
import tensorflow as tf

data = np.array([[  1. ,   2.1],
       [  2. ,   3. ],
       [  8.1, -10. ]])

d1 = tf.data.Dataset.from_tensor_slices(data)

print(d1)

In the example, `d1` is a dataset containing the data from `data`. The dataset consists of three observations, with each observation being a row in `data`. Since each row of `data` has two columns, the observations in `d1` have shape `(2,)`.

We can also create datasets from tuple inputs. This is useful when we want to create a dataset from both feature data and labels for each data observation.

Learn how datasets are represented in TensorFlow's input pipeline.

What you'll learn in this course

Data Pipeline

Model Execution

Dataset

Chapter Goals:

A. Input pipeline