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Course Outline
Introduction to AI/ML in Workflow Automation
- Overview of AI-driven automation
- Understanding AI/ML models for workflows
- Introduction to Make’s API and automation capabilities
Connecting AI/ML APIs to Make
- Using AI/ML services (OpenAI, Google Cloud AI, Hugging Face)
- Making API calls to AI models for automation
- Handling API authentication and security
Sentiment Analysis and Text Processing
- Extracting insights from customer feedback
- Using NLP models for text classification
- Automating response generation based on sentiment
Predictive Modeling and Decision Automation
- Using ML models for predictive analytics
- Automating decision-making based on AI predictions
- Integrating forecasting models into workflows
Automating Image and Video Processing
- Using AI for image recognition and classification
- Applying object detection in automation
- Automating content moderation and tagging
Optimizing AI-Driven Automation Workflows
- Handling errors and improving reliability
- Scaling AI integrations in Make
- Monitoring and maintaining AI-driven workflows
Testing and Debugging AI Integrations
- Using Postman for API testing
- Debugging AI/ML model responses
- Ensuring accuracy and consistency in automation
Summary and Next Steps
- Key takeaways from the course
- Resources for further learning
- Q&A and closing remarks
Requirements
- Experience using Make for workflow automation
- Understanding of APIs and webhooks
- Basic knowledge of AI/ML concepts and models
Audience
- AI/ML engineers
- Data scientists
- Tech innovators
14 Hours
