Machine Learning Agent

The Machine Learning Agent automates ML workflows, from data preprocessing to model deployment. It handles model selection, hyperparameter tuning, training monitoring, and provides intelligent feedback while maintaining conversation context and external memory.

Machine Learning Agent workflow and architecture

Configuration Parameters

Required Input Parameters

template: ML workflow template
input: User query or task description
session_id: Unique session identifier

Optional Configuration

llm: Language model configuration
- model_name: Name of the language model
- temperature: Response creativity (0-1)
- max_tokens: Maximum response length
tools: Available ML tools and libraries
- sklearn: Scikit-learn tools
- tensorflow: TensorFlow operations
- pytorch: PyTorch functions
- custom_tools: User-defined tools
external_memory: Memory configuration
- type: Memory storage type
- capacity: Maximum memory size
- persistence: Storage duration
sender_config: Message handling
- sender_type: Type of sender
- sender_name: Name identifier
- number_of_messages: Message limit
- order: Message processing order
agent_config: Agent behavior
- agent_description: Role description
- agent_instructions: Behavior guidelines
- handle_parse_errors: Error handling strategy
- verbose: Detailed logging
- max_iterations: Maximum processing iterations

Output Format

{
  "response": {
    "result": {
      "model_output": any,
      "metrics": object,
      "artifacts": array
    },
    "explanation": {
      "steps": array,
      "decisions": array,
      "visualizations": array
    },
    "recommendations": {
      "next_steps": array,
      "improvements": array
    }
  },
  "metadata": {
    "session_info": {
      "session_id": string,
      "timestamp": string,
      "duration": number
    },
    "memory_usage": {
      "current_size": number,
      "peak_size": number
    },
    "processing_stats": {
      "iterations": number,
      "errors_handled": number
    }
  }
}

Features

Automated ML workflow execution
Intelligent model selection
Hyperparameter optimization
Training monitoring
Error handling and recovery
Performance tracking
Memory management
Conversation context preservation

Note: Monitor memory usage when processing large datasets. Consider implementing checkpointing for long-running operations.

Tip: Use templates for common ML workflows and enable verbose mode during development for better debugging capabilities.

Example Usage

const mlAgent = new MLAgent({
  template: "classification_workflow",
  llm: {
    model_name: "gpt-4",
    temperature: 0.3
  },
  tools: ["sklearn", "pytorch"],
  external_memory: {
    type: "redis",
    capacity: "10GB"
  },
  sender_config: {
    sender_type: "user",
    sender_name: "data_scientist",
    number_of_messages: 100,
    order: "sequential"
  },
  agent_config: {
    agent_description: "ML workflow automation specialist",
    handle_parse_errors: true,
    verbose: true,
    max_iterations: 1000
  }
});

const response = await mlAgent.execute({
  input: "Train a classification model on customer churn data",
  session_id: "session_123"
});