You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

16. Other Books You May Enjoy

Hands-on exercise – building a data science environment using AWS services

In this hands-on exercise, you will create a data science environment using SageMaker with AWS CodeCommit as the source control.

Problem statement

As an ML Solutions Architect, you have been tasked with building a data science environment on AWS for the data scientists in the Equity Research department. The data scientists in the Equity Research department have several NLP problems, such as detecting the sentiment of financial phrases. Once you have created the environment for the data scientists, you also need to build a proof of concept to show the data scientists how to build and train an NLP model using the environment.

Dataset

The data scientists have indicated that they like to use the BERT model to solve sentiment analysis problems, and they plan to use the financial phrase dataset to establish some initial benchmarks for the model: https://www.kaggle.com/ankurzing/sentiment-analysis...