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Duration 14 hours
Course Outline
Introduction to the Application of Artificial Intelligence and Machine Learning in Process Automation
- General overview of AI-enabled automation frameworks
- Foundational understanding of AI and ML architectures for operational workflows
- Overview of the Make API and its functional automation capabilities
Establishing Connections Between AI/ML Services and the Make Platform
- Integration of third-party AI services, including OpenAI, Google Cloud AI, and Hugging Face
- Executing API requests to AI models to facilitate automated tasks
- Management of API credentials and adherence to security protocols
Application of Sentiment Analysis and Text Data Processing
- Deriving actionable insights from public and citizen feedback mechanisms
- Utilization of Natural Language Processing (NLP) models for document categorization
- Automation of response generation protocols based on sentiment indicators
Application of Predictive Modeling for Automated Decision Support
- Deployment of ML models to support predictive analytics in public sector operations
- Streamlining administrative decision-making processes through AI-driven forecasting
- Embedding forecasting capabilities into existing operational workflows
Automation of Image and Video Data Analysis
- Application of AI for visual recognition and classification of administrative records
- Utilization of object detection technologies within automated systems
- Standardization of content moderation and metadata tagging processes
Optimization of AI-Driven Operational Workflows
- Error mitigation strategies and enhancement of system reliability
- Scaling AI integrations within the Make platform for broader deployment
- Continuous monitoring and maintenance of AI-assisted workflows
Quality Assurance: Testing and Debugging of AI Integrations
- Utilization of Postman for rigorous API validation and testing
- Diagnostic procedures for AI/ML model outputs and responses
- Assurance of accuracy, consistency, and compliance in automated operations
Concluding Summary and Implementation Roadmap
- Summary of critical operational takeaways from the training
- Resources for continued professional development and learning
- Q&A session and final procedural remarks
Requirements
- Practical experience with Make for workflow automation
- Proficiency with API architectures and webhook mechanisms
- Foundational knowledge of AI/ML concepts and model structures
Target Audience
- AI/ML Engineers
- Data Scientists
- Technical Innovators
Testimonials (1)
real life examples
