Overview
The AI & ML Suite pack provides comprehensive coverage of artificial intelligence and machine learning workflows. From data science and model training to LLM architecture and prompt engineering — this pack equips you to build production AI systems. Perfect for ML engineers, AI researchers, data scientists, and teams building LLM-powered applications.Installation
Included Agents
ai-engineer
AI Systems EngineerEnd-to-end AI systems from model selection to production deployment, MLOps, and monitoring
data-scientist
Data Science ExpertStatistical analysis, predictive modeling, feature engineering, experimentation, and insights
ml-engineer
ML EngineeringProduction ML pipelines, model serving, automated retraining, monitoring, and performance tuning
llm-architect
LLM System DesignLLM architecture, fine-tuning, RAG systems, inference optimization, and evaluation frameworks
prompt-engineer
Prompt EngineeringPrompt design, optimization, analysis, chain-of-thought reasoning, and structured outputs
search-specialist
Search & ResearchAdvanced search techniques, information retrieval, multi-source synthesis, and web research
Who Should Use This Pack?
ML Engineers
ML Engineers
Build production ML systems with automated pipelines, model serving, and monitoring
AI Researchers
AI Researchers
Experiment with models, fine-tuning, and novel architectures
Data Scientists
Data Scientists
Analyze data, build predictive models, and derive insights from complex datasets
LLM Application Developers
LLM Application Developers
Build RAG systems, chatbots, and LLM-powered applications
Example Workflow
Here’s how to build an LLM-powered application using the AI pack:Key Capabilities
LLM Systems
- RAG (Retrieval-Augmented Generation) architecture
- Fine-tuning and model adaptation
- Prompt engineering and optimization
- Context window management
- Inference optimization and caching
Machine Learning
- Model training and hyperparameter tuning
- Feature engineering and selection
- Model evaluation and validation
- Automated retraining pipelines
- A/B testing frameworks
Data Science
- Exploratory data analysis
- Statistical modeling
- Predictive analytics
- Time series forecasting
- Causal inference
Production ML
- Model serving infrastructure
- Batch and real-time inference
- Model monitoring and drift detection
- MLOps automation
- Performance optimization
Common Use Cases
- RAG Application
- Predictive Model
- LLM Fine-Tuning
- Research Project
Agents: llm-architect → prompt-engineer → ml-engineer → ai-engineerBuild retrieval-augmented generation systems for Q&A, chatbots, or documentation search.
Tech Stack Coverage
| Area | Technologies | Agents |
|---|---|---|
| LLM | OpenAI, Anthropic, Llama, RAG, vector DBs | llm-architect, prompt-engineer |
| ML | PyTorch, TensorFlow, scikit-learn, XGBoost | ml-engineer, data-scientist |
| Data | Pandas, NumPy, SQL, Spark | data-scientist, search-specialist |
| MLOps | MLflow, Weights & Biases, Kubeflow | ml-engineer, ai-engineer |
| Serving | FastAPI, Ray Serve, TorchServe, Triton | ml-engineer, ai-engineer |
| Monitoring | Prometheus, Grafana, custom metrics | ai-engineer, ml-engineer |
LLM Application Patterns
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation)
Use llm-architect and prompt-engineer to build systems that retrieve relevant context before generating responses
Agentic Workflows
Agentic Workflows
Use llm-architect and ai-engineer to create autonomous agents that use tools and make decisions
Fine-Tuning
Fine-Tuning
Use llm-architect and ml-engineer to adapt foundation models to specific domains or tasks
Structured Outputs
Structured Outputs
Use prompt-engineer to generate JSON, SQL, or other structured formats reliably
ML Model Types
| Model Type | Use Cases | Key Agents |
|---|---|---|
| Classification | Spam detection, sentiment analysis, fraud detection | data-scientist, ml-engineer |
| Regression | Price prediction, forecasting, demand estimation | data-scientist, ml-engineer |
| Clustering | Customer segmentation, anomaly detection | data-scientist |
| Recommender | Product recommendations, content personalization | data-scientist, ml-engineer |
| NLP | Text classification, NER, summarization | llm-architect, prompt-engineer |
| Computer Vision | Image classification, object detection, segmentation | ai-engineer, ml-engineer |
Complementary Agents
Consider adding these agents for expanded capabilities:- data-engineer — Build ETL pipelines and data infrastructure
- mlops-engineer — Deep MLOps expertise for production systems
- python-pro — Advanced Python for ML development
- api-architect — Design APIs for model serving
- performance-engineer — Optimize inference latency
Next Steps
Install AI Pack
Explore Individual Agents
Browse detailed documentation for each agent
Data Stack Pack
Add data engineering for ETL and warehousing
ML to Production Pack
Alternative pack with MLOps and deployment focus