Small Language Models (SLMs) for Human-AI Interactions Training Course

This instructor-led live training session in US (delivered online or onsite) is designed for data scientists and developers at beginner to intermediate levels who aim to implement and leverage Small Language Models for government applications.

Upon completion, participants will possess the capability to:

• Understand the structural and functional attributes of Small Language Models.
• Apply SLMs to tasks such as text generation and sentiment analysis.
• Optimize and fine-tune SLMs for targeted use cases.
• Deploy SLMs in environments with restricted computational resources.
• Evaluate and interpret SLM performance within real-world operational contexts.

This instructor-led, live training in US (online or onsite) is intended for 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 US (online or onsite) is tailored for intermediate-level urban planners, city administrators, and smart city solution developers aiming to implement Small Language Models (SLMs) in municipal projects to improve urban living conditions.

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

• Understand the application of SLMs in smart cities.
• Integrate SLMs with urban data sets for enhanced decision-making.
• Develop strategies for deploying SLMs in urban management systems.
• Assess the impact of SLMs on urban planning and smart city solutions.

This instructor-led, live training in US (delivered online or onsite) is designed for intermediate-level data scientists and machine learning engineers seeking to develop and deploy small language models tailored for specific domains, including legal, medical, and technical fields.

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

• Comprehend the strategic importance and application of domain-specific language models.
• Curate and preprocess specialized datasets for effective model training.
• Train and fine-tune language models for domain-specific operational needs.
• Evaluate and benchmark models using domain-relevant performance metrics.
• Deploy domain-specific language models in practical, real-world scenarios.

This instructor-led, live training in US (online or onsite) is designed for advanced machine learning engineers and AI researchers seeking to develop energy-efficient AI solutions using small language models that are both high-performance and environmentally responsible.

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

• Analyze the impact of artificial intelligence on energy consumption and environmental sustainability.
• Implement model compression and optimization techniques to minimize the size and energy footprint of AI models.
• Utilize energy-efficient hardware and software frameworks for the deployment of AI systems for government use.
• Apply established best practices for sustainable AI development within public sector environments.
• Advocate for and support sustainable practices within the broader AI industry ecosystem.

This instructor-led, live training in US (available online or onsite) is designed for intermediate-level IT professionals tasked with deploying small language models directly onto devices with constrained processing power. This capability is essential for driving innovative applications across various sectors, particularly for government entities requiring secure, localized data processing.

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

• Address the technical challenges and implement solutions for running AI on compact hardware platforms.
• Optimize and compress AI models to ensure efficient deployment in resource-limited environments.
• Apply modern AI frameworks and tools to implement models locally on devices.
• Design and develop real-time AI applications suitable for mobile and IoT infrastructure.
• Assess and safeguard the security and privacy integrity of on-device AI systems, meeting the high standards required for government operations.