SLMs for Smart Cities: Urban Planning and Management with AI - Plan Szkolenia

This instructor-led, live training in Polsce (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

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

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

To prowadzone przez instruktora szkolenie na żywo w Polsce (na miejscu lub zdalnie) jest przeznaczone dla średnio zaawansowanych technologów edukacyjnych, projektantów instrukcji i programistów AI w edukacji, którzy chcą zintegrować Small Language Models (SLMs) z platformami edukacyjnymi w celu usprawnienia procesów nauczania i uczenia się.

Pod koniec tego szkolenia uczestnicy będą mogli

• Zrozumieć rolę SLM w technologii edukacyjnej.
• Projektować doświadczenia edukacyjne oparte na sztucznej inteligencji przy użyciu SLM.
• Wdrażać SLM w różnych środowiskach edukacyjnych.
• Ocenić skuteczność SLM w efektach uczenia się.

This instructor-led, live training in Polsce (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

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

• Understand the importance and application of domain-specific language models.
• Curate and preprocess specialized datasets for model training.
• Train and fine-tune language models for domain-specific applications.
• Evaluate and benchmark models using domain-relevant metrics.
• Deploy domain-specific language models in real-world scenarios.

This instructor-led, live training in Polsce (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally 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 Polsce (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 Polsce (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.
• Optimize and compress AI models for efficient on-device deployment.
• Utilize 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.