Advanced AI Systems Engineering Bootcamp by Tonex
This intensive 2-day bootcamp explores the advanced principles of AI systems engineering, focusing on scalability, optimization, and integration with cutting-edge technologies. Participants will learn to architect AI solutions, fine-tune large language models, optimize AI for edge devices, and enhance natural language processing capabilities. The program also covers AI integration with IoT and robotics, providing hands-on experience in deploying AI-driven applications. Designed for professionals seeking deep technical expertise, this bootcamp equips participants with the skills to build scalable, efficient, and innovative AI solutions for real-world applications.
Audience:
- AI engineers
- Data scientists
- Software architects
- IoT and robotics engineers
- Machine learning practitioners
- Technology leaders
Learning Objectives:
- Design scalable and optimized AI architectures
- Fine-tune large language models for custom applications
- Optimize AI models for efficient edge deployment
- Implement advanced NLP techniques for real-world applications
- Integrate AI with IoT and robotics for enhanced automation
Course Modules:
Module 1: Architecting AI Systems for Scalability
- Designing AI architectures for high-performance applications
- Managing data pipelines and model deployment
- Scaling AI systems with cloud and distributed computing
- Ensuring robustness and reliability in AI solutions
- Handling real-time AI inference and decision-making
- Case studies on scalable AI implementations
Module 2: Fine-Tuning LLMs with Custom Datasets
- Understanding LLM fine-tuning fundamentals
- Preprocessing and curating domain-specific datasets
- Applying transfer learning for better model adaptation
- Optimizing hyperparameters for efficient training
- Evaluating model performance and accuracy improvements
- Deployment strategies for fine-tuned LLMs
Module 3: AI Model Compression and Optimization for Edge Devices
- Techniques for reducing AI model size and complexity
- Quantization and pruning for efficient model deployment
- Edge computing frameworks for AI applications
- Balancing performance and energy efficiency
- Real-time inference on low-power hardware
- Deploying optimized AI models on edge devices
Module 4: Advanced NLP Techniques
- Implementing semantic search for knowledge retrieval
- Summarization techniques for automated content extraction
- Enhancing translation models for multilingual applications
- Sentiment analysis for business intelligence
- Leveraging transformers for contextual understanding
- NLP model deployment and performance tuning
Module 5: Integrating AI with IoT and Robotics
- AI-driven automation for smart devices
- Processing IoT sensor data with machine learning
- Real-time decision-making in robotic systems
- Enhancing autonomous navigation with AI
- Edge AI applications in IoT and robotics
- Security and reliability in AI-integrated systems
Module 6: AI Deployment and Real-World Applications
- Best practices for deploying AI solutions
- Addressing ethical concerns and bias in AI
- AI governance and compliance considerations
- Monitoring AI system performance post-deployment
- AI-driven business transformation strategies
- Future trends in AI systems engineering
Advance your AI expertise with this hands-on bootcamp. Gain practical experience in designing, fine-tuning, and deploying AI solutions. Enroll today to master cutting-edge AI engineering techniques!