Length: 2 Days
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Advanced AI Systems Engineering Bootcamp by Tonex

Component De-rating and Worst-Case Analysis for Critical Systems Training 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!

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