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 Duration 14 hours

Course Outline

Introduction to AI/ML in Workflow Automation

  • Overview of automation driven by AI.
  • Grasping AI/ML models suitable for workflow integration.
  • Exploring Make’s API and core automation features.

Linking AI/ML APIs to Make

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

Sentiment Analysis and Text Processing

  • Deriving insights from customer feedback data.
  • Leveraging NLP models for text classification tasks.
  • Automating response generation triggered by sentiment detection.

Predictive Modeling and Decision Automation

  • Employing ML models for predictive analytics.
  • Automating decision-making processes based on AI predictions.
  • Incorporating forecasting models into existing workflows.

Image and Video Processing Automation

  • Applying AI for image recognition and classification.
  • Implementing object detection within automation scenarios.
  • Automating content moderation and tagging systems.

Optimization of AI-Driven Automation Workflows

  • Managing errors and enhancing system reliability.
  • Scaling AI integrations within Make.
  • Monitoring and maintaining AI-powered workflows.

Testing and Debugging AI Integrations

  • Utilizing Postman for API testing purposes.
  • Troubleshooting and debugging AI/ML model responses.
  • Ensuring accuracy and consistency in automated processes.

Summary and Future Directions

  • Key insights gained from the course.
  • Resources for continued learning and development.
  • Q&A session and concluding remarks.

Requirements

  • Practical experience with Make for workflow automation.
  • Familiarity with APIs and webhooks.
  • Foundational knowledge of AI/ML concepts and models.

Target Audience

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

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