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Course Outline

Foundations of AI/ML in Workflow Automation

  • A broad overview of AI-powered automation.
  • Exploring AI/ML models tailored for workflow applications.
  • An introduction to Make’s API and automation features.

Integrating AI/ML APIs with Make

  • Utilizing AI/ML services such as OpenAI, Google Cloud AI, and Hugging Face.
  • Executing API calls to interact with AI models for automation tasks.
  • Managing API authentication and security protocols.

Sentiment Analysis and Text Processing

  • Deriving insights from customer feedback channels.
  • Applying NLP models for text classification tasks.
  • Automating response generation driven by sentiment analysis.

Predictive Modeling and Automated Decision-Making

  • Leveraging ML models for predictive analytics.
  • Automating decisions based on AI-generated predictions.
  • Embedding forecasting models into existing workflows.

Automating Image and Video Processing

  • Employing AI for image recognition and categorization.
  • Implementing object detection within automation pipelines.
  • Automating content moderation and tagging processes.

Optimization of AI-Driven Automation Workflows

  • Addressing errors and enhancing system reliability.
  • Scaling AI integrations within the Make platform.
  • Monitoring and maintaining AI-driven workflow performance.

Testing and Debugging AI Integrations

  • Utilizing Postman for comprehensive API testing.
  • Debugging responses from AI/ML models.
  • Guaranteeing accuracy and consistency in automated processes.

Summary and Future Directions

  • Essential takeaways from the course material.
  • Resources for continued professional development.
  • Q&A session and closing insights.

Requirements

  • Prior experience utilizing Make for workflow automation.
  • Familiarity with APIs and webhook mechanisms.
  • Foundational knowledge of AI/ML concepts and modeling.

Target Audience

  • AI/ML engineers.
  • Data scientists.
  • Technology innovators.
 14 Hours

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