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Course Outline
Foundations of AI/ML in Workflow Automation
- A broad overview of AI-powered automation.
- Exploring AI/ML models tailored for workflow applications.
- An introduction to Make’s API and automation features.
Integrating AI/ML APIs with Make
- Utilizing AI/ML services such as OpenAI, Google Cloud AI, and Hugging Face.
- Executing API calls to interact with AI models for automation tasks.
- Managing API authentication and security protocols.
Sentiment Analysis and Text Processing
- Deriving insights from customer feedback channels.
- Applying NLP models for text classification tasks.
- Automating response generation driven by sentiment analysis.
Predictive Modeling and Automated Decision-Making
- Leveraging ML models for predictive analytics.
- Automating decisions based on AI-generated predictions.
- Embedding forecasting models into existing workflows.
Automating Image and Video Processing
- Employing AI for image recognition and categorization.
- Implementing object detection within automation pipelines.
- Automating content moderation and tagging processes.
Optimization of AI-Driven Automation Workflows
- Addressing errors and enhancing system reliability.
- Scaling AI integrations within the Make platform.
- Monitoring and maintaining AI-driven workflow performance.
Testing and Debugging AI Integrations
- Utilizing Postman for comprehensive API testing.
- Debugging responses from AI/ML models.
- Guaranteeing accuracy and consistency in automated processes.
Summary and Future Directions
- Essential takeaways from the course material.
- Resources for continued professional development.
- Q&A session and closing insights.
Requirements
- Prior experience utilizing Make for workflow automation.
- Familiarity with APIs and webhook mechanisms.
- Foundational knowledge of AI/ML concepts and modeling.
Target Audience
- AI/ML engineers.
- Data scientists.
- Technology innovators.
14 Hours
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