ML Task Runner

A component that executes machine learning tasks using pre-defined models and configurations. The ML Task Runner simplifies the integration of machine learning capabilities into your workflows without requiring deep ML expertise.

ML Task Runner component interface and configuration

Resource Requirements: ML tasks can be compute-intensive. Ensure that your environment has sufficient resources for the selected ML task. Some complex models may require GPU acceleration for optimal performance.

Component Inputs

ML Task: Type of machine learning task to perform
Example: "Select a ML task" dropdown with options like "Image Classification", "Text Summarization", "Sentiment Analysis"
Select a task: Specific pre-configured model to use
Example: Selection from available models for the chosen task category
Tool Input: Data to process with the selected model
Example: Text for NLP tasks, image URL for vision tasks, data points for prediction tasks

Component Outputs

Tool Result: Results from the ML model execution
Example: Classification labels and confidence scores, generated text, detected entities, or predictions

Supported ML Task Categories

Computer Vision

Image and video processing tasks

Tasks include:
- Image Classification
- Object Detection
- Image Segmentation
- Face Recognition
- Pose Estimation

Natural Language Processing

Text processing and generation tasks

Tasks include:
- Text Classification
- Named Entity Recognition
- Sentiment Analysis
- Text Summarization
- Question Answering
- Language Translation

Tabular Data

Structured data analysis tasks

Tasks include:
- Regression
- Classification
- Anomaly Detection
- Time Series Forecasting
- Clustering

Multimodal

Tasks that combine multiple data types

Tasks include:
- Image Captioning
- Visual Question Answering
- Document Analysis
- Audio-Text Transcription

Implementation Example

// Example: Using ML Task Runner for sentiment analysis
async function analyzeFeedbackSentiment(customerFeedback) {
  // Run sentiment analysis on customer feedback
  const sentimentResult = await mlTaskRunnerComponent.execute({
    mlTask: "Natural Language Processing",
    selectTask: "Sentiment Analysis",
    toolInput: customerFeedback
  });
  
  // Process the results
  const sentiment = sentimentResult.toolResult;
  
  // Categorize feedback based on sentiment score
  let feedbackCategory;
  if (sentiment.score > 0.6) {
    feedbackCategory = "Positive";
  } else if (sentiment.score < 0.4) {
    feedbackCategory = "Negative";
  } else {
    feedbackCategory = "Neutral";
  }
  
  // Return the analysis result
  return {
    originalFeedback: customerFeedback,
    sentimentCategory: feedbackCategory,
    sentimentScore: sentiment.score,
    sentimentConfidence: sentiment.confidence,
    analysisTimestamp: new Date().toISOString()
  };
}

// Example: Using ML Task Runner for image classification
async function categorizeProductImage(imageUrl) {
  // Run image classification on product image
  const classificationResult = await mlTaskRunnerComponent.execute({
    mlTask: "Computer Vision",
    selectTask: "Image Classification",
    toolInput: imageUrl
  });
  
  // Extract classification labels and scores
  const classifications = classificationResult.toolResult;
  
  // Return the top 3 category predictions
  return {
    imageUrl: imageUrl,
    topCategories: classifications.slice(0, 3).map(c => ({
      category: c.label,
      confidence: c.score
    })),
    processedAt: new Date().toISOString()
  };
}

Use Cases

Content Moderation: Automatically detect and filter inappropriate content
Customer Feedback Analysis: Process and categorize customer reviews and feedback
Data Enrichment: Extract insights and metadata from unstructured content
Document Processing: Extract information from documents and forms
Content Recommendation: Generate personalized content recommendations
Predictive Analytics: Forecast trends and patterns in business data

Useful Resources

Best Practices

Ensure input data matches the expected format for the selected ML task
Consider pre-processing data before sending it to ML models
Use appropriate task-specific models for better accuracy
Implement error handling for cases where ML models might fail
Consider resource requirements when choosing complex ML tasks
Validate ML outputs before using them in critical applications
Monitor model performance and results for potential drift or degradation
Use confidence scores to filter low-confidence predictions when appropriate

Documentation