Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Table of Contents (17 chapters)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Hands-on exercise – training a TensorFlow model

In this exercise, you will learn how to install the TensorFlow library in your local Jupyter environment and build and train a simple neural network model. Launch a Jupyter notebook that you have previously installed on your machine. If you don't remember how to do this, visit the Hands-on lab section of Chapter 3, Machine Learning Algorithms.

Once the Jupyter notebook is running, create a new folder by selecting the New dropdown and then Folder. Rename the folder TensorFlowLab. Open the TensorFlowLab folder, create a new notebook inside this folder, and rename the notebook Tensorflow-lab1.ipynb. Now, let's get started:

  1. Inside the first cell, run the following code to install TensorFlow:
    ! pip3 install --upgrade tensorflow
  2. Now, we must import the library and load the sample training data. We will use the built-in fashion_mnist dataset that comes with the keras library to do so. Next, we must load the data...