NobleProg Training Place, City Albuquerque, NM - Regus at Marquette Avenue

Albuquerque, NM - Regus at Marquette Avenue

500 Marquette Ave NW, Albuquerque, United States, 87102

The venue is easily accessible by car via I‑25 and I‑40, with ample parking available in nearby public garages and surface lots. For those arriving from out of town, Albuquerque International Sunport is located just 5 miles away, providing convenient air travel access. Additionally, the location is well-served by public transit, within walking distance of several ABQ RIDE bus routes and near the Downtown Rail Runner station.

Santa Fe, NM - Regus at Washington Avenue

150 Washington Avenue, Santa Fe, United States, 87501

The venue is easily accessible by car via I‑25, with secure parking available both on site and in nearby private garages. For those traveling by air, Albuquerque International Sunport (ABQ) is located approximately 60 miles to the south, with shuttle and rental car options readily available. Public transit is also a convenient option, with multiple Santa Fe Trails bus routes and the Rail Runner shuttle stop within walking distance.

Explore Our Courses

SLMs for Smart Cities: Urban Planning and Management with AI

 14 Hours

This instructor-led, live training in New Mexico (online or onsite) is aimed at intermediate-level urban planners, city administrators, and smart city solution developers who wish to implement Small Language Models (SLMs) in smart city projects to improve urban living.

By the end 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.
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SLMs for Educational Tech: Tailoring AI for Learning and Development

 14 Hours

This instructor-led, live training in New Mexico (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.
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Small Language Models (SLMs) for On-Device AI

 21 Hours

This instructor-led, live training in New Mexico (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.
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Small Language Models (SLMs): Developing Energy-Efficient AI

 21 Hours

This instructor-led, live training in New Mexico (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.
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Small Language Models (SLMs) for Human-AI Interactions

 14 Hours

This instructor-led, live training in New Mexico (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.
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Small Language Models (SLMs) for Domain-Specific Applications

 28 Hours

This instructor-led, live training in New Mexico (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.
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Small Language Models (SLMs): Applications and Innovations

 14 Hours

This instructor-led, live training in New Mexico (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.
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