The Machine Learning Solutions Architect Handbook
David Ping
- 440 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
The Machine Learning Solutions Architect Handbook
David Ping
About This Book
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions
Key Features
- Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
- Build an efficient data science environment for data exploration, model building, and model training
- Learn how to implement bias detection, privacy, and explainability in ML model development
Book Description
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.
What you will learn
- Apply ML methodologies to solve business problems
- Design a practical enterprise ML platform architecture
- Implement MLOps for ML workflow automation
- Build an end-to-end data management architecture using AWS
- Train large-scale ML models and optimize model inference latency
- Create a business application using an AI service and a custom ML model
- Use AWS services to detect data and model bias and explain models
Who this book is for
This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
]]>
Frequently asked questions
Information
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
- Chapter 1, Machine Learning and Machine Learning Solutions Architecture
- Chapter 2, Business Use Cases for Machine Learning
Chapter 1: Machine Learning and Machine Learning Solutions Architecture
- What is ML, and how does it work?
- The ML life cycle and its key challenges
- What is ML solutions architecture, and where does it fit in the overall life cycle?
What are AI and ML?
- Supervised ML
- Unsupervised machine learning
- Reinforcement learning
Supervised ML
- Classifying documents into different document types automatically, as part of a document management workflow. The typical business benefits of ML-based document processing are the reduction of manual effort, which reduces costs, faster processing time, and higher processing quality.
- Assessing the sentiment of news articles to help understand the market perception of a brand or product or facilitate investment decisions.
- Automating the objects or faces detection in images as part of a media image processing workflow. The business benefits this delivers are cost-saving from the reduction of human labor, faster processing, and higher accuracy.
- Predicting the probability that someone will default on a bank loan. The business benefits this delivers are faster decision-making on loan application reviews and approvals, lower processing costs, and a reduced impact on a company's financial statement due to loan defaults.