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Duration 14 hours
Course Outline
Introduction to AI/ML in Workflow Automation
- Overview of automation driven by AI.
- Grasping AI/ML models suitable for workflow integration.
- Exploring Make’s API and core automation features.
Linking AI/ML APIs to Make
- Utilizing AI/ML services such as OpenAI, Google Cloud AI, and Hugging Face.
- Executing API calls to AI models for automated tasks.
- Managing API authentication and ensuring security.
Sentiment Analysis and Text Processing
- Deriving insights from customer feedback data.
- Leveraging NLP models for text classification tasks.
- Automating response generation triggered by sentiment detection.
Predictive Modeling and Decision Automation
- Employing ML models for predictive analytics.
- Automating decision-making processes based on AI predictions.
- Incorporating forecasting models into existing workflows.
Image and Video Processing Automation
- Applying AI for image recognition and classification.
- Implementing object detection within automation scenarios.
- Automating content moderation and tagging systems.
Optimization of AI-Driven Automation Workflows
- Managing errors and enhancing system reliability.
- Scaling AI integrations within Make.
- Monitoring and maintaining AI-powered workflows.
Testing and Debugging AI Integrations
- Utilizing Postman for API testing purposes.
- Troubleshooting and debugging AI/ML model responses.
- Ensuring accuracy and consistency in automated processes.
Summary and Future Directions
- Key insights gained from the course.
- Resources for continued learning and development.
- Q&A session and concluding remarks.
Requirements
- Practical experience with Make for workflow automation.
- Familiarity with APIs and webhooks.
- Foundational knowledge of AI/ML concepts and models.
Target Audience
- AI/ML engineers.
- Data scientists.
- Technology innovators.
Testimonials (1)
real life examples