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Course Outline
Introduction to AI and ML in Workflow Automation
- Overview of AI-driven automation.
- Understanding AI and ML models for workflows.
- Introduction to Make’s API and automation capabilities.
Connecting AI and ML APIs to Make
- Utilising AI and ML services (such as OpenAI, Google Cloud AI, and Hugging Face).
- Making API calls to AI models for automation purposes.
- Handling API authentication and security protocols.
Sentiment Analysis and Text Processing
- Extracting insights from customer feedback.
- Using NLP models for text classification.
- Automating response generation based on sentiment.
Predictive Modelling 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.
Optimising 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 and 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
- Practical experience using Make for workflow automation.
- Solid understanding of APIs and webhooks.
- Foundational knowledge of AI and ML concepts and models.
Target Audience
- AI and ML engineers.
- Data scientists.
- Technology innovators.
14 Hours
Testimonials (1)
real life examples
