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Machine Learning for Business
Using Amazon SageMaker and Jupyter
Doug Hudgeon, Richard Nichol
  • December 2019
  • ISBN 9781617295836
  • 280 pages
  • printed in black & white

A clear and well-explained set of practical examples that demonstrates how to solve everyday problems, suitable for technical and nontechnical readers alike.

John Bassil, Fethr
  • Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen
  • Think about the benefits of forecasting tedious business processes and back-office tasks
  • Envision quickly gauging customer sentiment from social media content (even large volumes of it).
  • Consider the competitive advantage of making decisions when you know the most likely future events
Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started!
Table of Contents detailed table of contents

Part 1: Machine learning in business

1 How machine learning applies to your business

1.1 Why are our business systems so terrible?

1.2 Why is automation important now?

1.2.1 What is productivity?

1.2.2 How will machine learning improve productivity?

1.3 How do machines make decisions?

1.3.1 People: Rules-based or not?

1.3.2 Can I trust a pattern-based answer?

1.3.3 How can machine learning improve our business systems?

1.4 Can a machine help Karen make decisions?

1.4.1 Target variables

1.4.2 Features

1.5 How does a machine learn?

1.6 Getting approval in your company to use machine learning to make decisions

1.7 The tools

1.7.1 What is AWS and SageMaker, and how can they help me?

1.7.2 What is a Jupyter notebook?

1.8 Setting up SageMaker in preparation for tackling the scenarios in chapters 2 through 7

1.9 The time to act is now

Summary

Part 2: Six scenarios: Machine learning for business

2 Should you send a purchase order to a technical approver?

2.1 The decision

2.2 The data

2.3 Putting on your training wheels

2.4 Run the Jupyter notebook and make predictions

2.4.1 Part 1: Load and examine the data

2.4.2 Part 2: Getting the data into the right shape

2.4.3 Part 3: Create training, validation, and test datasets

2.4.4 Part 4: Training the model

2.4.5 Part 5: Hosting the model

2.4.6 Part 6: Testing the model

2.5 Deleting the endpoint and shutting down your notebook instance

2.5.1 Deleting the endpoint

2.5.2 Shutting down the notebook instance

Summary

3 Should you call a customer because they are at risk of churning?

3.1 What are you making decisions about?

3.2 The process flow

3.3 Preparing the dataset

3.3.1 Transformation 1: Normalize the data

3.3.2 Transformation 2: Calculate the change from week to week

3.4 XGBoost primer

3.4.1 How XGBoost works

3.4.2 How does the machine learning model determine whether the function is getting better or getting worse?

3.5 Getting ready to build the model

3.5.1 Upload a dataset to S3

3.5.2 Set up a notebook on SageMaker

3.6 Building the model

3.6.1 Part 1: Loading and examining the data

3.6.2 Part 2: Getting the data into the right shape

3.6.3 Part 3: Creating training, validation, and test datasets

3.6.4 Part 4: Training the model

3.6.5 Part 5: Hosting the model

3.6.6 Part 6: Testing the model

3.7 Deleting the endpoint and shutting down your notebook instance

3.7.1 Deleting the endpoint

3.7.2 Shutting down the notebook instance

3.8 Checking to make sure the endpoint is deleted

Summary

4 Should an incident be escalated to your support team?

4.1 What are you making decisions about?

4.2 The process flow

4.3 Preparing the dataset

4.4 NLP (natural language processing)

4.4.1 Creating word vectors

4.4.2 Deciding how many words to include in each group

4.5 What is BlazingText and how does it work?

4.6 Getting ready to build the model

4.6.1 Upload a dataset to S3

4.6.2 Set up a notebook on SageMaker

4.1 What are you making decisions about?

4.7.1 Part 1: Loading and examining the data

4.7.2 Part 2: Getting the data into the right shape

4.7.3 Part 3: Creating training and validation datasets

4.7.4 Part 4: Training the model

4.7.5 Part 5: Hosting the model

4.7.6 Part 6: Testing the model

4.8 Deleting the endpoint and shutting down your notebook instance

4.8.1 Deleting the endpoint

4.9 Checking to make sure the endpoint is deleted

Summary

5 Should you question an invoice sent by a supplier?

5.1 What are you making decisions about?

5.2 The process flow

5.3 Preparing the dataset

5.4 What are anomalies

5.5 Supervised versus unsupervised machine learning

5.6 What is Random Cut Forest and how does it work?

5.6.1 Sample 1

5.6.2 Sample 2

5.7 Getting ready to build the model

5.7.1 Upload a dataset to S3

5.7.2 Set up a notebook on SageMaker

5.8 Building the model

5.8.1 Part 1: Load and examine the data

5.8.2 Part 2: Getting the data into the right shape

5.8.3 Part 3: Create training and validation datasets

5.8.4 Part 4: Training the model

5.8.5 Part 5: Hosting the model

5.8.6 Part 6: Testing the model

5.9 Deleting the endpoint and shutting down your notebook instance

5.9.1 Deleting the endpoint

5.9.2 Shutting down the notebook instance

5.10 Checking to make sure the endpoint is deleted

Summary

6 Forecasting your company’s monthly power usage

6.1 What are you making decisions about?

6.1.1 Introduction to time-series data

6.1.2 Kiara’s time-series data: Daily power consumption

6.2 Load the Jupyter notebook for working with time-series data

6.3 Preparing the dataset: Charting time-series data

6.4 What is a neural network?

6.5 Getting ready to build the model

6.5.1 Upload a dataset to S3

6.5.2 Set up a notebook on SageMaker

6.6 Building the model

6.6.1 Part 1: Load and examine the data

6.6.2 Part 2: Getting the data into the right shape

6.6.3 Part 3: Create training and test datasets

6.6.4 Part 4: Training the model

6.6.5 Part 5: Hosting the model

6.6.6 Part 6: Make predictions and plot results

6.7 Deleting the endpoint and shutting down your notebook instance

6.7.1 Deleting the endpoint

6.7.1 Shutting down the notebook instance

6.8 Checking to make sure the endpoint is deleted

Summary

7 Improving your company’s monthly power usage forecast

7.1 DeepAR’s ability to pick up periodic events

7.3 Incorporating additional datasets into Kiara’s power consumption model

7.4 Getting ready to build the model

7.4.1 Download the notebook we prepared

7.4.2 Set up the folder on SageMaker

7.4.3 Upload notebook to SageMaker

7.4.4 Download the datasets from the S3 bucket

7.4.5 Set up a folder on S3 to hold your data

7.4.6 Upload the datasets to your AWS bucket

7.5 Building the model

7.5.1 Part 1: Setting up the notebook

7.5.2 Part 2: Importing the datasets

7.5.3 Part 3: Getting the data into the right shape

7.5.4 Part 4: Create training and test datasets

7.5.5 Part 5: Configure the model and set up the server to build the model

7.5.6 Part 6: Make predictions and plot results

7.6 Deleting the endpoint and shutting down your notebook instance

7.6.1 Deleting the endpoint

7.6.2 Shutting down the notebook instance

7.7 Checking to make sure the endpoint is deleted

Summary

Part 3: Moving machine learning into production

8 Serving predictions over the web

8.1 Why is serving decisions and predictions over the web so difficult?

8.2 Overview of steps for this chapter

8.3 The SageMaker endpoint

8.4 Set up the SageMaker endpoint

8.4.1 Uploading the notebook

8.4.2 Uploading the data

8.4.3 Running the notebook and creating the endpoint

8.5 Setting up the serverless API endpoint

8.5.1 Setting up your AWS credentials on your AWS account

8.5.2 Setting up your AWS credentials on your local computer

8.5.3 Configuring your credentials

8.6 Creating the web endpoint

8.6.1 Installing Chalice

8.6.2 Creating a Hello World API

8.6.3 Adding the code that serves the SageMaker endpoint

8.6.4 Configuring permissions

8.6.5 Updating requirements.txt

8.6.6 Deploying Chalice

8.7 Serving decisions

Summary

9 Case studies

9.1 Case study 1: WorkPac

9.1.1 Designing the project

9.1.2 Stage 1: Preparing and testing the model

9.1.3 Stage 2: Implementing proof of concept

9.1.4 Stage 3: Embedding the process into the company’s operations

9.1.5 Next steps

9.1.6 Lessons learned

9.2 Case study 2: Faethm

9.2.1 AI at the core

9.2.2 Using machine learning to improve processes at Faethm

9.2.3 Stage 1: Get the data

9.2.4 Stage 2: Identify the features

9.2.5 Stage 3: Validate the results

9.2.6 Stage 4: Implement in production

9.3 Conclusion

Summary

Appendixes

Appendix A: Signing up for Amazon AWS

A.1 Signing up for AWS

A.2 AWS Billing overview

Appendix B: Setting up and using S3 to store files

B.1 Set up a bucket in S3

B.1.1 Step 1: Naming your bucket

B.1.2 Step 2: Setting properties for your bucket

B.1.3 Step 3: Setting permissions

B.1.4 Step 4: Reviewing settings

B.2 Setting up folders in S3

B.3 Uploading files to S3

Appendix C: Setting up and using AWS SageMaker to build a machine learning system

C.1 Getting set up

C.2 Start at the Dashboard

C.3 Creating a notebook instance

C.4 Starting the notebook instance

C.5 Uploading the notebook to the notebook instance

C.6 Running the notebook

Appendix D: Shutting it all down

D.1 Deleting the endpoint

D.2 Shutting down the notebook instance

Appendix E: Installing Python

About the Technology

Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how.

About the book

Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results.

What's inside

  • Identifying tasks suited to machine learning
  • Automating back office processes
  • Using open source and cloud-based tools
  • Relevant case studies

About the reader

For technically inclined business professionals or business application developers.

About the authors

Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size.

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