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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
- Utilizing AI/ML services (OpenAI, Google Cloud AI, Hugging Face).
- Making API calls to AI models for automation purposes.
- Managing 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
- Leveraging 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
- Prior experience with Make for workflow automation.
- Familiarity with APIs and webhooks.
- Fundamental knowledge of AI/ML concepts and models.
Target Audience
- AI/ML engineers.
- Data scientists.
- Tech innovators.
14 Hours
Testimonials (1)
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