SCADEMY - Secure Coding Academy

Description

Curious about the potential of AI in software development?

Participants of this course will gain a comprehensive understanding of AI coding assistants, prompt engineering, and practical applications through hands-on exercises and real-world case studies. As they progress, participants will employ AI-assisted tools to build an application utilizing the building blocks mastered throughout the program.

Participants will also train machine learning models and deploy these together with foundation models in local machines as well as in the cloud, acquiring essential future-proof skills in the process.

Upon successful completion, attendees will have the knowledge and practical experience to use AI assistants during their software development process confidently and effectively utilize custom machine learning models in the location of their choice.

Participants attending this course will

Understand the basics of artificial intelligence
Be able to identify and explain the different forms of AI
Utilize language models on a higher level with prompt engineering
Have the knowledge to use AI tools to increase productivity and efficiency
Gain practical experience using AI tools during development
Be able to deploy models in the cloud and locally
Be capable of training and deploying machine learning models

Outline

Understanding AI
AI and software development
Prompt engineering
Becoming more productive using AI tools
Building an app with AI assistance
Working with models
Machine learning

Course information

Preparedness

General software development

Exercises

Hands-on

Delivery methods

Onsite / Virtual classroom

Course reviews

I really liked the vibe of the course. Both tutors were friendly and supportive.

February 2025, Online

I especially liked the hands-on approach, with practical coding exercises and applications that made complex AI concepts easy to understand.

February 2025, Online

The exercises. We tried a lot things, and like this, its easier to learn, and understand.

February 2025, Online

View all reviews

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Day 1
Day 2
Day 3

AI in the software development industry
- Introduction
  - Impact of AI on tech industry
  - Reaction of the big players
  - AI won't take your job (yet)
  - From simple questions to working code
The AI landscape
- Welcome to the AI Revolution
  - Is this magic?
- Demo - Github Copilot
  - Your AI Pair Programmer
  - What can it do?
  - Writing Unit Tests
  - Writing Regular Expressions
- Demo - Amazon Q Developer
  - AI Code Generator
- Tabnine - Tabnine
  - Code completion
  - Tabnine chat
- Exercise: Cloud VM Intro
  - Introduction
  - Lab Guide
- Exercise: OpenAI DALL-E 3 Introduction
  - Generating images with simple prompts
  - Calling DALL-E 3 from the command prompt
- Demo - Stable Diffusion XL
  - Capabilities
Developer Plugins
- Demo - Advanced Data Analytics
  - Advanced Data Analytics
  - Math examples
  - Upload and download files with data analysis
  - Create and interpret code
Reasoning models
Further important AI concepts
- Fine-tuning and embedding
  - Fine-tuning
  - Embeddings
Prompt Engineering
- Fundamentals summarized
  - The 5 rules for prompting
- Intermediate techniques summarized
  - Intermediate techniques summarized
- Advanced techniques summarized
  - Advanced techniques summarized
- Advanced techniques - SudoLang: pseudocode for LLMs
  - Introducing SudoLang
  - SudoLang examples
  - Behind the tech
  - A SudoLang program
  - Integrating an LLM
  - Integrating an LLM with SudoLang
- Exercise: Translate a prompt to SudoLang
  - A long prompt
  - A different solution
- Limitations in development
  - Limitations of ChatGPT
  - Example - hallucinated answer
  - Example - the correct function
Copilot ecosystem
- GitHub Copilot
  - Inside GitHub Copilot
  - Prompt Engineering inside Copilot
  - Fill-In-the-Middle (FIM)
  - Life of a Completion
  - Copilot over the network
  - Improved semantic understanding with embedding
  - Features and use-cases
  - Productivity increase
  - Boosted coding speed
  - Other types of Copilot
- GitHub Copilot - Autocomplete
  - Most trivial feature: autocomplete
  - Basic usage
- Exercise: Setting up GitHub Copilot
  - Log in
- Exercise: GitHub Copilot autocomplete
  - GitHub Copilot autocomplete
- GitHub Copilot - Prompt Engineering
  - Code generation from comments
- Exercise: Prompt Engineering in Copilot
  - Prompt Engineering in Copilot
- GitHub Copilot - Testing
  - Writing unit tests
- Exercise: Testing with Github Copilot
  - First tests for an Express app
- GitHub Copilot - Unfamiliar territory and boilerplate
  - Boilerplate code generation
  - Unfamiliar framework or libraries
- Exercise: Writing boilerplate code
  - Write boilerplate code
  - Express boilerplate with Copilot
- Exercise: Unfamiliar framework or libraries
  - Calling an API
- GitHub Copilot - Copilot Chat
  - Getting Started with Copilot Chat
  - Features of Copilot Chat
- GitHub Copilot - Admin options
  - Copilot settings 1/2
  - Copilot settings 2/2
  - Copilot permissions
Coding tools
- AI-assissted coding tools
  - Cursor Composer
  - Accepting Suggestion
  - Built-in Chat

Copilot ecosystem
- Exercise: ChatGPT in your IDE
  - CodeGPT
- Copilot Limitations
- Amazon Q Developer
  - More Than Just a Coding Assistant
  - From Legacy to Modern Code in Minutes
  - Enhanced Code Security
  - Language And IDE Compatibility
  - Confidential Data
- Tabnine
  - Code privacy
  - Local machine mode
  - Tabnine for teams
  - Writing unit tests
  - Tabnine chat
- Comparison
  - Comparison
Building an AI integrated application
- Fresh Cart
  - Introducing Fresh Cart
  - The vision for Fresh Cart 2.0
  - How will we get there?
- Exercise: Fresh Cart Setup
  - Setting up the exercise
  - The perfect output
  - Fill the database with 100 ingredients
- Fresh Cart - Integrating an LLM chatbot
  - OpenAI API introduction
  - Context crafting and streaming
- Exercise: Fresh Cart chatbot
- Exercise: Recipe chatbot
  - Recipe chatbot
- Fresh Cart - Prompt routing
  - Using multiple contexts
  - From routing to orchestration
- Exercise: Prompt routing
  - Prompt orchestration summary
- Exercise: Output formatting
- Fresh Cart - Basic Semantic Search
  - What is semantic search?
  - Semantic search vs. classical search?
  - Semantic search vs. classical search?
- Exercise: Semantic search
- Fresh Cart - State mutation
  - Mutate state based on semantics
- Fresh Cart - Next steps
  - Improvements
  - Upcoming features
- Operating an AiLLM
  - Logging
  - Self correction
Attacks on AI systems
- Attacks on AI systems - Prompt injection
  - Prompt injection
  - Impact
  - Examples
  - Indirect prompt injection
  - From prompt injection to phishing
- Exercise: Red Teaming - Prompt injection (Levels 1-2)
- Attacks on AI systems - Model jailbreaks
  - What's a model jailbreak?
  - How jailbreaks work?
- Jailbreaking ChatGPT
  - The most famous ChatGPT jailbreak
  - The 6.0 DAN prompt
  - AutoDAN - Generating Stealthy Jailbreak Prompts
- Exercise: Red Teaming - Jailbreaking (Levels 3-5)
- Attacks on AI systems - Prompt extraction
  - Prompt extraction
- Exercise: Red Teaming - Prompt extraction (Levels 6-7)
- Defending AI systems - Prompt injection defenses
  - Intermediate techniques
  - Advanced techniques
  - More Security APIs
  - ReBuff example
  - Llama Guard example I
  - Llama Guard example II
  - Lakera example
- Attempts against a similar exercise
  - Gandalf from Lakera
  - Types of Gandalf exploits
- Exercise: Red Teaming - Give it your best shot!
Working with models
- Foundation Models
  - What is a foundation model?
  - Foundation Models in practice
- Open-source models
  - Open-source models vs. closed-source models
  - Examples of open-source models
- Open-source models - Meta - Llama 3
  - Variants
  - Providers
  - Running a model locally
- Exercise: Running a model locally
  - (Almost) any model on your machine
- Open-source models - Stable Diffusion
  - Stability AI
  - Reminder: How diffusion models work
  - Stable Diffusion
  - Latent Diffusion model (LDM)
  - Advantages of using LDM

Working with models
- Open-source models - HuggingFace 🤗
  - Introduction
  - Models
  - Chat Markup Language (ChatML)
  - Chat templates
  - Instruction formats
  - HuggingFace API in action
  - Types of models
  - Model properties
  - Datasets
  - Spaces
  - The Open LLM Leaderboard
  - Benchmarks
  - AutoTrain
- Exercise: AutoTrain
  - Comparing fine-tuned models
  - A larger training set isn't always better
  - AutoTrain
- Transformers - Hugging Face
  - What is Transformers?
  - Transformers Pipelines
  - Text Generation Pipeline
- Exercise: Transformers
  - Using the Transformers pipeline
  - Using the Transformers pipeline solution
- Inference Endpoints
  - The easiest way to deploy AI models
  - Inference Endpoints UI
  - The supported models of Inference Endpoints
- Exercise: Inference Endpoints
  - OpenChat with Inference Endpoints
  - Sending a request to the Endpoint
- Other models
  - BERT
  - Falcon family
  - OpenChat 3.5
  - Tons of other text-based models
  - CLIP - Connecting text and images
- Cloud providers - Microsoft Azure AI Tools
  - Azure AI services
  - Azure OpenAI Studio
  - Hosting a model
- Cloud providers - Google Cloud Platform
  - Google Vertex AI
  - PaLM 2
  - Vertex AI - Generative AI Studio
  - Vertex AI - Model Garden
- Cloud providers - Amazon AI Tools
  - Amazon Bedrock
  - Other Amazon tools for building generative AI applicatons
- Cloud providers - Replicate
  - Cloud platform for machine learning
  - Cog - Containers for machine learning
- Exercise: Cog containers
  - Running a model with Cog
Machine Learning
- TensorFlow
  - Open-source machine learning platform
  - Keras
  - The main concepts of TensorFlow
  - Training loop in Keras
  - Machine learning on other devices
  - Kaggle Models / Model Garden
- Exercise: Training a model
  - Custom training
  - Saving and loading
  - Hyperparameter tuning
  - TensorFlow Serving with Docker
Advanced AI concepts
- Fine-tuning
  - What is fine-tuning?
  - Parameters and hyperparameters
  - Behind the tech - How to fine-tune a model?
  - Customizing OpenAI models
  - Start a fine-tuning in OpenAI Playground
  - Fine-tuning with Vertex AI
  - Fine-tuning with Vertex AI - RLHF
  - Fine-tuning on AWS
- Exercise: Fine-tuning
  - Fine-tuning with OpenAI Playground
- Embedding
  - What is embedding?
  - Vector space
  - Euclidean distance (L2)
  - Euclidean distance in high-dimensions
  - The change of distance in high-dimensions
  - Cosine similarity
  - Dot product
  - Vector databases and indexes
  - Use cases
- Retrieval Augmented Generation
  - RAG basics
  - RAG in action
- Exercise: Consensus.app
  - What is Consensus.app?
  - Research like you never did before
- Exercise: Search with Perplexity AI
  - Research with Perplexity AI
- Exercise: Finding similar products in FreshCart
  - Explore the embedding flow
  - Embedding products
  - Query for similar products
- Predicted Output - OpenAI
  - Predicted Output

AI for Software Developers

Description

Participants attending this course will

Outline

Course information

Preparedness

Exercises

Delivery methods

Course reviews

Related courses

Table of contents

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