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

This live, instructor-led training is available in Latvia (either online or on-site) and is tailored for data scientists and developers at beginner to intermediate levels looking to implement and utilize Small Language Models across various applications.

Upon completing this training, participants will be equipped to:

• Comprehend the architecture and functionality of Small Language Models.
• Deploy SLMs for tasks such as text generation and sentiment analysis.
• Optimize and fine-tune SLMs for specific use cases.
• Integrate SLMs into resource-constrained environments.
• Evaluate and interpret the performance of SLMs in real-world scenarios.

This live, instructor-led training session, available in Latvia (either online or on-site), targets intermediate-level educational technologists, instructional designers, and AI developers in the education sector who seek to integrate Small Language Models (SLMs) into their platforms to improve teaching and learning processes.

Upon completing this training, participants will be equipped to:

• Grasp the function of SLMs within educational technology.
• Structure AI-powered learning experiences utilizing SLMs.
• Deploy SLMs across diverse educational environments.
• Assess the impact of SLMs on student learning outcomes.

This live, instructor-led training in Latvia (online or on-site) is tailored for mid-level data scientists and machine learning engineers seeking to build and deploy small language models optimized for particular industries, such as legal, medical, and technical domains.

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

• Grasp the significance and practical application of industry-specific language models.
• Assemble and preprocess specialized datasets required for model training.
• Execute training and fine-tuning of language models for targeted domain applications.
• Assess and benchmark model performance using relevant industry metrics.
• Implement domain-specific language models within real-world operational environments.

This live, instructor-led training, conducted in Latvia (available online or onsite), is tailored for senior machine learning engineers and AI researchers seeking to build powerful yet eco-friendly AI solutions using Small Language Models (SLMs).

By the conclusion of this course, learners will be capable of:

• Evaluating how AI influences energy use and environmental health.
• Applying compression and optimization techniques to shrink model size and lower energy consumption.
• Utilizing energy-conscious hardware and software ecosystems for AI deployment.
• Adopting best practices for developing sustainable AI systems.
• Promoting and participating in sustainable initiatives within the AI sector.

This live, instructor-led training in Latvia (online or onsite) is tailored for intermediate-level data scientists, machine learning professionals, and AI researchers aiming to create engaging and efficient AI-powered conversational experiences utilizing small language models.

By the end of this session, participants will have the ability to:

• Understand the basics 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 live, instructor-led course, conducted in Latvia (online or on-site), is designed for intermediate-level IT professionals looking to implement small language models directly on devices with constrained processing capabilities, thereby enabling novel applications across multiple sectors.

By the conclusion of this training, participants will be equipped to:

• Address the technical hurdles and apply solutions for AI implementation on compact hardware.
• Optimize and reduce the size of AI models for streamlined on-device operation.
• Apply modern AI frameworks and utilities for on-device model execution.
• Create and develop real-time AI solutions for mobile and IoT platforms.
• Verify and uphold the security and privacy standards of on-device AI architectures.