SLMs for Smart Cities: Urban Planning and Management with AI Training Course

This instructor-led, live training in Malaysia (online or onsite) targets beginner to intermediate-level data scientists and developers aiming to implement and utilise Small Language Models across various applications.

Upon completion of this training, participants will be equipped to:

• Grasp the architecture and functionality of Small Language Models.
• Implement SLMs for tasks including text generation and sentiment analysis.
• Optimize and fine-tune SLMs for particular use cases.
• Deploy SLMs within resource-constrained environments.
• Evaluate and interpret the performance of SLMs in real-world scenarios.

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level educational technologists, instructional designers, and AI developers in education who wish to integrate Small Language Models (SLMs) into educational platforms to enhance teaching and learning processes.

By the end of this training, participants will be able to:

• Understand the role of SLMs in educational technology.
• Design AI-driven learning experiences using SLMs.
• Implement SLMs in various educational settings.
• Evaluate the effectiveness of SLMs in learning outcomes.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level data scientists and machine learning engineers who aim to develop and implement small language models customised for specific sectors, such as law, healthcare, and technology.

Upon completion of this training, participants will be equipped to:

• Grasp the significance and practical applications of domain-specific language models.
• Select and preprocess specialised datasets for model training.
• Train and fine-tune language models for use in specific domains.
• Assess and benchmark models using metrics relevant to the specific domain.
• Deploy domain-specific language models in practical, real-world scenarios.

This instructor-led live training in Malaysia (online or onsite) targets advanced-level machine learning engineers and AI researchers who aim to develop energy-efficient AI solutions using small language models that are both powerful and eco-friendly.

By the end of this training, participants will be able to:

• Understand the impact of AI on energy consumption and the environment.
• Apply model compression and optimization techniques to reduce the size and energy usage of AI models.
• Utilize energy-efficient hardware and software frameworks for AI deployment.
• Implement best practices for sustainable AI development.
• Advocate for and contribute to sustainable practices in the AI industry.

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

• Understand the fundamentals of conversational AI and the role of SLMs.
• Design and implement user-centric AI interactions.
• Develop and train SLMs for interactive applications.
• Evaluate and improve the effectiveness of human-AI communication using appropriate metrics.
• Deploy scalable and ethical AI-driven conversational interfaces in real-world scenarios.

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

• Understand the challenges and solutions for implementing AI on compact hardware.
• Optimise and compress AI models for efficient on-device deployment.
• Utilise modern AI frameworks and tools for on-device model implementation.
• Design and develop real-time AI applications for mobile and IoT devices.
• Evaluate and ensure the security and privacy of on-device AI systems.