Building and Deploying AI Applications

• 1.1 Why prompt engineering comes first
• 1.2 How AI assistants produce answers
• 1.3 The major assistants
• 1.4 The anatomy of a prompt
• 1.5 Prompting techniques
• 1.6 AI in your coding environment
• 1.7 Calling models from your own code
• 1.8 Using AI responsibly

Take the Chapter 1 assessment

• 2.1 What a computer does
• 2.2 Source code, compilers and interpreters
• 2.3 Installing your tools
• 2.4 The terminal
• 2.5 Reading documentation
• 2.6 Using AI coding assistants responsibly

Take the Chapter 2 assessment

• 3.1 Literals, variables and names
• 3.2 Numbers
• 3.3 Strings
• 3.4 Booleans and logic
• 3.5 Operator precedence and evaluation
• 3.6 Type conversion, input and output

Take the Chapter 3 assessment

• 4.1 Sequence, selection and repetition
• 4.2 Selection with if, elif and else
• 4.3 Repetition with while and for
• 4.4 Pseudocode and flowcharts
• 4.5 Proving that loops are correct
• 4.6 Tracing and off-by-one errors
• 4.7 Counting steps: an introduction to complexity
• 4.8 Case study: a fee instalment schedule

Take the Chapter 4 assessment

• 5.1 Lists and tuples
• 5.2 Dictionaries and sets
• 5.3 Nested structures and JSON
• 5.4 Comprehensions and generator expressions
• 5.5 Choosing a structure

Take the Chapter 5 assessment

• 6.1 Defining functions
• 6.2 Parameters in depth
• 6.3 Scope
• 6.4 Recursion
• 6.5 Modules and packages
• 6.6 Exceptions
• 6.7 Testing

Take the Chapter 6 assessment

• 7.1 Classes and instances
• 7.2 Encapsulation and invariants
• 7.3 Inheritance, polymorphism and composition
• 7.4 Special methods and the data model
• 7.5 Dataclasses
• 7.6 UML class diagrams

Take the Chapter 7 assessment

• 8.1 Text and binary files
• 8.2 Structured text formats
• 8.3 Working with the file system
• 8.4 Logging

Take the Chapter 8 assessment

• 9.1 Layered models
• 9.2 IP addresses
• 9.3 Ports and sockets
• 9.4 The Domain Name System
• 9.5 TCP and UDP

Take the Chapter 9 assessment

• 10.1 Requests and responses
• 10.2 URLs
• 10.3 Statelessness, cookies and sessions
• 10.4 HTTPS and TLS
• 10.5 A web server from raw sockets
• 10.6 Python's http.server
• 10.7 Inspecting traffic

Take the Chapter 10 assessment

• 11.1 HTML: structure and meaning
• 11.2 CSS: presentation
• 11.3 JavaScript: behaviour
• 11.4 The DOM and fetch
• 11.5 Accessibility

Take the Chapter 11 assessment

• 12.1 The WSGI model
• 12.2 A first Flask application
• 12.3 Routing
• 12.4 The request and response
• 12.5 Templates with Jinja2
• 12.6 Static files and project layout
• 12.7 Blueprints and the application factory

Take the Chapter 12 assessment

• 13.1 Handling forms
• 13.2 Cookies
• 13.3 Sessions
• 13.4 Passwords
• 13.5 Logging in and staying logged in
• 13.6 Post/Redirect/Get
• 13.7 Cross-site request forgery

Take the Chapter 13 assessment

• 14.1 The relational model
• 14.2 SQL
• 14.3 Designing a schema: normalisation
• 14.4 Indexes and query cost
• 14.5 Transactions
• 14.6 SQLite in development, PostgreSQL in production
• 14.7 SQLAlchemy and Flask-SQLAlchemy

Take the Chapter 14 assessment

• 15.1 REST principles
• 15.2 Designing JSON APIs
• 15.3 FastAPI
• 15.4 Automatic documentation
• 15.5 Asynchronous programming
• 15.6 Authentication with tokens
• 15.7 Testing APIs

Take the Chapter 15 assessment

• 16.1 The life of a deployed model
• 16.2 Training and saving a model
• 16.3 Prediction endpoints
• 16.4 Validation and versioning
• 16.5 Latency, throughput and batching
• 16.6 Case study: a crop-disease classifier behind an API

Take the Chapter 16 assessment

• 17.1 Why build a desktop GUI
• 17.2 Event-driven programming
• 17.3 Widgets
• 17.4 Layouts
• 17.5 Signals and slots
• 17.6 Qt Designer and .ui files
• 17.7 Threads in a GUI
• 17.8 Packaging with PyInstaller
• 17.9 Case study: a desktop client for the prediction API

Take the Chapter 17 assessment

• 18.1 Why version control
• 18.2 How Git stores history
• 18.3 Recording changes
• 18.4 Branching and merging
• 18.5 Remotes and GitHub
• 18.6 What belongs in a repository
• 18.7 Team workflows

Take the Chapter 18 assessment

• 19.1 From laptop to server
• 19.2 Deploying to Render
• 19.3 AWS essentials
• 19.4 Application servers and reverse proxies
• 19.5 Domains, DNS and HTTPS
• 19.6 Containers with Docker
• 19.7 Monitoring, logs and costs

Take the Chapter 19 assessment

• 20.1 Goals and risk
• 20.2 The OWASP Top 10
• 20.3 Injection
• 20.4 Cross-site scripting
• 20.5 Secrets, passwords and least privilege
• 20.6 Backups and incident response

Take the Chapter 20 assessment

• 21.1 What AI, machine learning and robotics are
• 21.2 A map of AI tools
• 21.3 No-code AI workflows
• 21.4 Evaluating AI outputs
• 21.5 Ethics, privacy and data protection

Take the Chapter 21 assessment

• 22.1 Inside a language model
• 22.2 Structured outputs and templates
• 22.3 Iterating and testing prompts
• 22.4 Calling an LLM API from Python
• 22.5 Prompt injection and safe use

Take the Chapter 22 assessment