AI for Software Developers
Vill du lära dig mer om potentialen för AI inom mjukvaruutveckling?
Den här kursen ger dig en omfattande förståelse för AI-kodningsassistenter, promptteknik och praktiska tillämpningar genom övningar och verkliga fallstudier. Du får använda AI-verktyg för att utveckla en applikation med hjälp av byggstenarna du lär dig under kursen.
Efter kursen har du kunskap och praktisk erfarenhet för att självständigt använda AI-assistenter under programutvecklingsprocessen och effektivt skapa olika typer av applikationer på en bråkdel av tiden jämfört med traditionella metoder.
Du kommer även att träna maskininlärningsmodeller och driftsätta dessa tillsammans med grundläggande modeller på lokala maskiner samt i molnet, och därigenom få kunskaper du behöver för framtiden.
Målgrupp och förkunskaper
Den här kursen vänder sig till systemutvecklare som vill lära sig att utnyttja AI-verktyg.
För att kunna delta i utbildningen behöver du erfarenhet av och allmänna kunskaper om systemutveckling. Dessutom behöver du allmän kunskap om artificiell intelligens och viss erfarenhet av att skriva textprompter för generativ AI.
Kursens mål
- Förstå grunderna inom Artificiell Intelligens
- Identifiera och förklara olika typer av AI
- Använda språkmodeller på en högre nivå med promptteknik
- Använda AI-verktyg för att öka produktivitet och effektivitet
- Få praktisk erfarenhet av att använda AI-verktyg under utvecklingen
- Kunna distribuera modeller i molnet och lokalt
- Vara kapabel att träna och distribuera maskininlärningsmodeller
Detaljerad information
Kursmaterialet är på engelska, med detta innehåll:
Day 1
AI in the software development industry
- Impact of AI on tech industry
- Reaction of the big players
- AI won't take your job (yet)
- From simple questions to working code
- AI improves software development
- AI will transform software as we know it
- Connection between AI and software developers
The AI landscape
- Welcome to the AI Revolution
- Demo - Github Copilot
- Your AI Pair Programmer
- What can it do?
- Writing Unit Tests
- Writing Regular Expressions
- Demo - Amazon CodeWhisperer
- AI Code Generator
- Tabnine – Tabnine
- Code completion
- Tabnine chat
- Exercise: Cloud VM Intro
- Exercise: OpenAI DALL-E 2 Introduction
- Generating images with simple prompts
- Calling DALL-E from the command prompt
- Demo - Stable Diffusion XL
Developer Plugins
- Demo - Advanced Data Analytics
- How to enable Advanced Data Analytics?
- Math examples
- Upload and download files with data analysis
- Create and interpret code
Further important AI concepts
- Fine-tuning and embedding
Prompt Engineering
- Fundamentals summarized
- The 5 rules for prompting
- Intermediate 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
- Tree of Thoughts - Advanced techniques
- How does it work?
- How can you experiment with this?
- Exercise: Tree of Thoughts with LangChain framework
- 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
- GitHub Copilot – Autocomplete
- Most trivial feature: autocomplete
- Basic usage
- Exercise: Setting up GitHub Copilot
- Exercise: GitHub Copilot autocomplete
- GitHub Copilot - Prompt Engineering
- Code generation from comments
- Exercise: Prompt Engineering in Copilot
Day 2
Copilot ecosystem
- 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
- Exercise: ChatGPT in your IDE
- CodeGPT
- Copilot Limitations
- Amazon CodeWhisperer
- Code with confidence
- Enhance code security
- Language and IDE compatibility
- Tabnine
- Code privacy
- Local machine mode
- Tabnine for teams
- Writing unit tests
- Comparison
- Choose your fighter wisely
Building "Fresh Cart" - 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
- 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?
- 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 - Prompt injection
- Prompt injection
- Impact
- Examples
- Indirect prompt injection
- From prompt injection to phishing
- Exercise: Prompt injection exercise
- Prompt injection exercise Get the password!
Day 3
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 2
- Variants
- Providers
- Running a model locally
- Exercise: Running LLama 2 locally
- LLama 2 at home
- LLama 2 in the VM
- Interact with a local model
- Open-source models - Stable Diffusion
- Stability AI
- Reminder: How diffusion models work
- Stable Diffusion
- Latent Diffusion model (LDM)
- Advantages of using LDM
- Exercise: Local Stable Diffusion
- Running a model using OnnxStream
- Open-source models - HuggingFace
- Introduction
- Models
- 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
- Transformers - Hugging Face
- What is Transformers?
- Transformers Pipelines
- 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
Summary and closing - What did we learn?
- Coding assistants summarized
- AI-integrated applications summary
- Open-source models summary
- Cloud providers summary
- Machine learning summary
- Advanced concepts summarized
Kursen hålls på begäran - Kontakta oss för mer information.
Telefon: 08-562 557 50 E-post: kursbokning@cornerstone.se
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