,Contents
1. Preface
1. Who this book is for
2. What this book covers
3. To get the most out of this book
4. Get in touch
1. Navigating the ML Lifecycle with ML Solutions Architecture
1. ML versus traditional software
2. ML lifecycle
1. Business problem understanding and ML problem framing
2. Data understanding and data preparation
3. Model training and evaluation
4. Model deployment
5. Model monitoring
6. Business metric tracking
3. ML challenges
4. ML solutions architecture
1. Business understanding and ML transformation
2. Identification and verification of ML techniques
3. System architecture design and implementation
4. ML platform workflow automation
5. Security and compliance
5. Summary
2. Exploring ML Business Use Cases
1. ML use cases in financial services
1. Capital market front office
1. Sales trading and research
2. Investment banking
3. Wealth management
2. Capital market back office operations
1. Net Asset Value review
2. Post-trade settlement failure prediction
3. Risk management and fraud
1. Anti-money laundering
2. Trade surveillance
3. Credit risk
4. Insurance
1. Insurance underwriting
2. Insurance claim management
2. ML use cases in media and entertainment
1. Content development and production
2. Content management and discovery
3. Content distribution and customer engagement
3. ML use cases in healthcare and life sciences
, 1. Medical imaging analysis
2. Drug discovery
3. Healthcare data management
4. ML use cases in manufacturing
1. Engineering and product design
2. Manufacturing operations – product quality and yield
3. Manufacturing operations – machine maintenance
5. ML use cases in retail
1. Product search and discovery
2. Targeted marketing
3. Sentiment analysis
4. Product demand forecasting
6. ML use cases in the automotive industry
1. Autonomous vehicles
1. Perception and localization
2. Decision and planning
3. Control
2. Advanced driver assistance systems (ADAS)
7. Summary
3. Exploring ML Algorithms
1. Technical requirements
2. How machines learn
3. Overview of ML algorithms
1. Consideration for choosing ML algorithms
2. Algorithms for classification and regression problems
1. Linear regression algorithms
2. Logistic regression algorithms
3. Decision tree algorithms
4. Random forest algorithm
5. Gradient boosting machine and XGBoost algorithms
6. K-nearest neighbor algorithm
7. Multi-layer perceptron (MLP) networks
3. Algorithms for clustering
4. Algorithms for time series analysis
1. ARIMA algorithm
2. DeepAR algorithm
5. Algorithms for recommendation
1. Collaborative filtering algorithm
2. Multi-armed bandit/contextual bandit algorithm
6. Algorithms for computer vision problems
1. Convolutional neural networks
2. ResNet
7. Algorithms for natural language processing (NLP) problems
1. Word2Vec
2. BERT
8. Generative AI algorithms
, 1. Generative adversarial network
2. Generative pre-trained transformer (GPT)
3. Large Language Model
4. Diffusion model
4. Hands-on exercise
1. Problem statement
2. Dataset description
3. Setting up a Jupyter Notebook environment
4. Running the exercise
5. Summary
4. Data Management for ML
1. Technical requirements
2. Data management considerations for ML
3. Data management architecture for ML
1. Data storage and management
1. AWS Lake Formation
2. Data ingestion
1. Kinesis Firehose
2. AWS Glue
3. AWS Lambda
3. Data cataloging
1. AWS Glue Data Catalog
2. Custom data catalog solution
4. Data processing
5. ML data versioning
1. S3 partitions
2. Versioned S3 buckets
3. Purpose-built data version tools
6. ML feature stores
7. Data serving for client consumption
1. Consumption via API
2. Consumption via data copy
8. Special databases for ML
1. Vector databases
2. Graph databases
9. Data pipelines
10. Authentication and authorization
11. Data governance
1. Data lineage
2. Other data governance measures
4. Hands-on exercise – data management for ML
1. Creating a data lake using Lake Formation
2. Creating a data ingestion pipeline
3. Creating a Glue Data Catalog
4. Discovering and querying data in the data lake
5. Creating an Amazon Glue ETL job to process data for ML
1. Preface
1. Who this book is for
2. What this book covers
3. To get the most out of this book
4. Get in touch
1. Navigating the ML Lifecycle with ML Solutions Architecture
1. ML versus traditional software
2. ML lifecycle
1. Business problem understanding and ML problem framing
2. Data understanding and data preparation
3. Model training and evaluation
4. Model deployment
5. Model monitoring
6. Business metric tracking
3. ML challenges
4. ML solutions architecture
1. Business understanding and ML transformation
2. Identification and verification of ML techniques
3. System architecture design and implementation
4. ML platform workflow automation
5. Security and compliance
5. Summary
2. Exploring ML Business Use Cases
1. ML use cases in financial services
1. Capital market front office
1. Sales trading and research
2. Investment banking
3. Wealth management
2. Capital market back office operations
1. Net Asset Value review
2. Post-trade settlement failure prediction
3. Risk management and fraud
1. Anti-money laundering
2. Trade surveillance
3. Credit risk
4. Insurance
1. Insurance underwriting
2. Insurance claim management
2. ML use cases in media and entertainment
1. Content development and production
2. Content management and discovery
3. Content distribution and customer engagement
3. ML use cases in healthcare and life sciences
, 1. Medical imaging analysis
2. Drug discovery
3. Healthcare data management
4. ML use cases in manufacturing
1. Engineering and product design
2. Manufacturing operations – product quality and yield
3. Manufacturing operations – machine maintenance
5. ML use cases in retail
1. Product search and discovery
2. Targeted marketing
3. Sentiment analysis
4. Product demand forecasting
6. ML use cases in the automotive industry
1. Autonomous vehicles
1. Perception and localization
2. Decision and planning
3. Control
2. Advanced driver assistance systems (ADAS)
7. Summary
3. Exploring ML Algorithms
1. Technical requirements
2. How machines learn
3. Overview of ML algorithms
1. Consideration for choosing ML algorithms
2. Algorithms for classification and regression problems
1. Linear regression algorithms
2. Logistic regression algorithms
3. Decision tree algorithms
4. Random forest algorithm
5. Gradient boosting machine and XGBoost algorithms
6. K-nearest neighbor algorithm
7. Multi-layer perceptron (MLP) networks
3. Algorithms for clustering
4. Algorithms for time series analysis
1. ARIMA algorithm
2. DeepAR algorithm
5. Algorithms for recommendation
1. Collaborative filtering algorithm
2. Multi-armed bandit/contextual bandit algorithm
6. Algorithms for computer vision problems
1. Convolutional neural networks
2. ResNet
7. Algorithms for natural language processing (NLP) problems
1. Word2Vec
2. BERT
8. Generative AI algorithms
, 1. Generative adversarial network
2. Generative pre-trained transformer (GPT)
3. Large Language Model
4. Diffusion model
4. Hands-on exercise
1. Problem statement
2. Dataset description
3. Setting up a Jupyter Notebook environment
4. Running the exercise
5. Summary
4. Data Management for ML
1. Technical requirements
2. Data management considerations for ML
3. Data management architecture for ML
1. Data storage and management
1. AWS Lake Formation
2. Data ingestion
1. Kinesis Firehose
2. AWS Glue
3. AWS Lambda
3. Data cataloging
1. AWS Glue Data Catalog
2. Custom data catalog solution
4. Data processing
5. ML data versioning
1. S3 partitions
2. Versioned S3 buckets
3. Purpose-built data version tools
6. ML feature stores
7. Data serving for client consumption
1. Consumption via API
2. Consumption via data copy
8. Special databases for ML
1. Vector databases
2. Graph databases
9. Data pipelines
10. Authentication and authorization
11. Data governance
1. Data lineage
2. Other data governance measures
4. Hands-on exercise – data management for ML
1. Creating a data lake using Lake Formation
2. Creating a data ingestion pipeline
3. Creating a Glue Data Catalog
4. Discovering and querying data in the data lake
5. Creating an Amazon Glue ETL job to process data for ML
