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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
- Leveraging AI/ML services such as OpenAI, Google Cloud AI, and Hugging Face.
- Executing API calls to AI models for automation purposes.
- Managing API authentication and security protocols.
Sentiment Analysis and Text Processing
- Extracting insights from customer feedback.
- Utilizing NLP models for text classification.
- Automating response generation based on sentiment analysis.
Predictive Modeling and Decision Automation
- Applying ML models for predictive analytics.
- Automating decision-making processes based on AI predictions.
- Integrating forecasting models into workflows.
Automating Image and Video Processing
- Employing AI for image recognition and classification.
- Applying object detection within automation scenarios.
- Automating content moderation and tagging tasks.
Optimizing AI-Driven Automation Workflows
- Handling errors and enhancing system reliability.
- Scaling AI integrations within 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 outputs.
Summary and Next Steps
- Key takeaways from the course.
- Resources for further learning.
- Q&A session and closing remarks.
Requirements
- Prior experience using Make for workflow automation.
- A solid understanding of APIs and webhooks.
- Foundational knowledge of AI/ML concepts and models.
Target Audience
- AI/ML engineers.
- Data scientists.
- Tech innovators.
14 Hours
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