Online or onsite, instructor-led live Large Language Models (LLMs) training courses demonstrate through interactive hands-on practice how to use Large Language Models for various natural language tasks.
LLMs training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Dartmouth onsite live Large Language Models (LLMs) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg also offers bespoke Large Language Models (LLMs) consultancy services in Dartmouth. Our consultants have helped hundreds of clients around the world get unstuck. Our clients value our highly-personalized consulting approach and find consulting to be well-suited for complex long-term projects, short-term projects requiring niche expertise, urgent problem fixing, critical knowledge transfer, and team coaching and support. To learn more about our past consultancy engagements, see consultancy case studies.
If instead you need people for continuous projects, NobleProg can support your organisation with a full range of staff. Whether your needs are for medium-term or long-term assignments, entry-level or highly-skilled expertise, single-person or multi-person personnel, our interim staffing / staff augmentation solutions can provide you with the talent needed to complete your most challenging projects. Contact us for more information.
NobleProg -- Your Local Training Provider
Dartmouth - Metropolitan Place
99 Wyse Road, Dartmouth, Canada, B3A 4S5
Metropolitan Place is an ocean-front building, close to Alderney Landing and home to leading businesses. Motorway exits and the local ferry terminal are located close by, and restaurants cafes are easily accessible for lunch.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level AI researchers, data scientists, and developers who wish to understand, fine-tune, and implement Meta AI's Large Language Models for various NLP applications.
By the end of this training, participants will be able to:
Understand the architecture and functioning of Meta AI's Large Language Models.
Set up and fine-tune Meta AI LLMs for specific use cases.
Implement LLM-based applications such as text summarization, chatbots, and sentiment analysis.
Optimize and deploy large language models efficiently.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level AI professionals, business analysts, and technology leaders who wish to understand the principles of generative AI and the applications of LLMs in business settings. Participants will learn about transformers, prompt engineering, and ethical considerations in deploying these models for real-world solutions.
By the end of this training, participants will be able to:
Understand the underlying principles of generative AI and large language models.
Implement and fine-tune LLMs for specific business applications.
Apply prompt engineering techniques for optimal model outputs.
Recognize ethical considerations and manage risks in LLM deployment.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level AI professionals and ethicists, data scientists and engineers, and policy makers and stakeholders who wish to understand and navigate the ethical landscape of LLMs.
By the end of this training, participants will be able to:
Identify ethical issues and challenges associated with LLMs.
Apply ethical frameworks and principles to LLM deployment.
Assess the societal impact of LLMs and mitigate potential risks.
Develop strategies for responsible AI development and usage.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level NLP practitioners and data scientists, content creators and translators, and global businesses who wish to use LLMs for language translation and creating multilingual content.
By the end of this training, participants will be able to:
Understand the principles of cross-lingual learning and translation with LLMs.
Implement LLMs for translating content between various languages.
Create and manage multilingual datasets for training LLMs.
Develop strategies for maintaining consistency and quality in translation.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level financial analysts, data scientists, and investment professionals who wish to leverage LLMs for financial market analysis and prediction.
By the end of this training, participants will be able to:
Understand the application of LLMs in financial market analysis.
Use LLMs to process financial news, reports, and data for market insights.
Develop predictive models for stock prices, market trends, and economic indicators.
Integrate LLM insights into investment decision-making processes.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level environmental scientists and researchers, data analysts, and policy makers and environmental advocates who wish to use LLMs for environmental modeling and analysis.
By the end of this training, participants will be able to:
Understand the application of LLMs in environmental science.
Utilize LLMs to analyze and model environmental data.
Interpret LLM outputs for environmental impact assessments.
Communicate findings effectively to inform policy and conservation efforts.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level VR and AR developers, game designers, and AI engineers who wish to incorporate LLMs into VR and AR applications to create more engaging and responsive environments.
By the end of this training, participants will be able to:
Understand the role of LLMs in creating immersive VR and AR experiences.
Develop VR and AR applications that utilize LLMs for interactive dialogues and content creation.
Integrate LLMs with VR and AR development tools for enhanced user engagement.
Apply best practices for designing AI-driven narratives and interactions in virtual spaces.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists, machine learning engineers, and software developers who wish to apply Large Language Models (LLMs) to multimodal data for advanced AI applications.
By the end of this training, participants will be able to:
Understand the principles of multimodal learning with LLMs.
Implement LLMs to process and analyze text, image, and audio data.
Develop applications that leverage the strengths of multimodal data integration.
Evaluate the performance of multimodal LLM systems.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level cybersecurity professionals and data scientists who wish to utilize LLMs for enhancing cybersecurity measures and threat intelligence.
By the end of this training, participants will be able to:
Understand the role of LLMs in cybersecurity.
Implement LLMs for threat detection and analysis.
Utilize LLMs for security automation and response.
Integrate LLMs with existing security infrastructure.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists and business analysts who wish to utilize large language models (LLMs) to forecast trends and behaviors in various industries.
By the end of this training, participants will be able to:
Understand the fundamentals of LLMs and their role in predictive analytics.
Implement LLMs to analyze and forecast data in various industries.
Evaluate the effectiveness of predictive models using LLMs.
Integrate LLMs with existing data processing pipelines.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).
By the end of this training, participants will be able to:
Understand the components and functionality of transformer models.
Optimize and fine-tune LLMs for specific tasks and applications.
Understand the core principles and methodologies of reinforcement learning.
Learn how reinforcement learning techniques can enhance the performance of LLMs.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level content creators, marketers, and educational technologists who wish to harness the power of LLMs for generating high-quality, diverse, and engaging content across various domains.
By the end of this training, participants will be able to:
Understand the capabilities of LLMs and their application in content generation.
Set up and use LLMs for generating various types of content.
Apply best practices for prompting and fine-tuning LLMs to produce desired outputs.
Evaluate the quality of AI-generated content and refine it for specific audiences.
Explore advanced techniques for creative and multi-modal content generation with LLMs.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at educators, EdTech professionals, and researchers with varying levels of experience and expertise who wish to leverage LLMs for creating personalized educational experiences.
By the end of this training, participants will be able to:
Understand the architecture and capabilities of LLMs.
Identify opportunities for personalization in educational content using LLMs.
Design adaptive learning platforms that utilize LLMs for content personalization.
Implement LLM-driven strategies for enhancing student engagement and learning outcomes.
Evaluate the effectiveness of LLMs in educational settings and make data-driven decisions for
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level software developers and data scientists who wish to implement LLMs in speech recognition and synthesis systems.
By the end of this training, participants will be able to:
Understand the role of LLMs in speech technologies.
Implement LLMs for accurate speech recognition and natural-sounding speech synthesis.
Integrate LLMs with speech recognition engines and speech synthesizers.
Evaluate and improve the performance of speech systems using LLMs.
Stay informed about current trends and future directions in speech technologies.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level customer support and IT professionals who wish to implement LLMs to create responsive and intelligent customer support chatbots.
By the end of this training, participants will be able to:
Understand the fundamentals and architecture of Large Language Models (LLMs).
Design and integrate LLMs into customer support systems.
Enhance the responsiveness and user experience of chatbots.
Address ethical considerations and ensure compliance with industry standards.
Deploy and maintain an LLM-based chatbot for real-world applications.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data and marketing professionals who wish to apply LLMs to analyze and interpret public sentiment from various text sources such as social media posts, product reviews, and customer feedback.
By the end of this training, participants will be able to:
Understand the principles of sentiment analysis and its application using LLMs.
Preprocess and prepare datasets for sentiment analysis.
Train and fine-tune LLMs to accurately reflect sentiment in text.
Analyze sentiment in real-time from social media and other text sources.
Integrate sentiment analysis findings into business strategies and decision-making processes.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level software developers and technical writers who wish to leverage LLMs to streamline their coding workflow and create detailed, comprehensive documentation.
By the end of this training, participants will be able to:
Understand the role of LLMs in automating code generation and software documentation.
Utilize LLMs to create accurate and efficient code snippets and documentation.
Integrate LLMs into their software development lifecycle for enhanced productivity.
Maintain high-quality documentation standards using automated tools.
Address ethical considerations and best practices for using AI in software development.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level business professionals and data analysts who wish to harness the power of LLMs for extracting business insights.
By the end of this training, participants will be able to:
Understand the fundamentals and applications of LLMs in the context of business intelligence.
Apply LLMs to analyze large datasets and extract meaningful insights.
Integrate LLM-driven analytics into strategic business decision-making processes.
Evaluate the ethical considerations and best practices for using LLMs in business.
Anticipate future trends in AI and prepare for the evolving landscape of business intelligence.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.
By the end of this training, participants will be able to:
Set up and configure LlamaIndex for use with LLMs.
Index and query custom datasets using LlamaIndex to enhance LLM functionality.
Design and develop sophisticated applications that utilize LlamaIndex and LLMs.
Understand and apply best practices for working with LLMs and LlamaIndex.
Navigate the ethical considerations involved in deploying LLM-powered applications.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level AI researchers and policy makers who wish to explore the ethical implications of AI development and learn how to apply ethical guidelines when building AI solutions with LangChain.
By the end of this training, participants will be able to:
Identify key ethical issues in AI development with LangChain.
Understand the impact of AI on society and decision-making processes.
Develop strategies for building fair and transparent AI systems.
Implement ethical AI guidelines into LangChain-based projects.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.
By the end of this training, participants will be able to:
Understand the principles and components of LlamaIndex.
Ingest and structure data for use with LLMs.
Implement context augmentation to improve AI model performance.
Integrate LlamaIndex into existing AI systems and workflows.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level web developers and UX designers who wish to leverage LangChain to create intuitive and user-friendly web applications.
By the end of this training, participants will be able to:
Understand the fundamental concepts of LangChain and its role in enhancing web user experience.
Implement LangChain in web apps to create dynamic and responsive interfaces.
Integrate APIs into web apps to improve interactivity and user engagement.
Optimize user experience using LangChain’s advanced customization features.
Analyze user behavior data to fine-tune web app performance and experience.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.
By the end of this training, participants will be able to:
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.
By the end of this training, participants will be able to:
Understand the principles of prompt engineering and few-shot learning.
Design effective prompts for various NLP tasks.
Leverage few-shot techniques to adapt LLMs with minimal data.
Optimize LLM performance for practical applications.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level business analysts and automation engineers who wish to understand how to use LangChain and APIs for automating repetitive tasks and workflows.
By the end of this training, participants will be able to:
Understand the basics of API integration with LangChain.
Automate repetitive workflows using LangChain and Python.
Utilize LangChain to connect various APIs for efficient business processes.
Create and automate custom workflows using APIs and LangChain’s automation capabilities.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.
By the end of this training, participants will be able to:
Understand the role of AI in content creation.
Set up and use Google Gemini AI to generate and optimize content.
Apply text-to-text transformations to produce creative and original content.
Implement SEO strategies using AI-driven insights.
Analyze content performance and adapt strategies using Gemini AI.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.
By the end of this training, participants will be able to:
Understand the impact of AI on customer service.
Set up Google Gemini AI to automate and personalize customer interactions.
Utilize text-to-text and image-to-text transformations to improve service efficiency.
Develop AI-driven strategies for real-time customer feedback analysis.
Explore advanced features to create a seamless customer service experience.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data professionals who wish to use LangChain to enhance their data analysis and visualization capabilities.
By the end of this training, participants will be able to:
Automate data retrieval and cleaning using LangChain.
Conduct advanced data analysis using Python and LangChain.
Create visualizations with Matplotlib and other Python libraries integrated with LangChain.
Leverage LangChain for generating natural language insights from data analysis.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.
By the end of this training, participants will be able to:
Understand the fundamentals of large language models.
Set up and use Google Gemini AI for various AI tasks.
Implement text-to-text and image-to-text transformations.
Build basic AI-driven applications.
Explore advanced features and customization options in Google Gemini AI.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level data engineers and DevOps professionals who wish to leverage LangChain's capabilities by integrating it with various cloud services.
By the end of this training, participants will be able to:
Integrate LangChain with major cloud platforms such as AWS, Azure, and Google Cloud.
Utilize cloud-based APIs and services to enhance LangChain-powered applications.
Scale and deploy conversational agents to the cloud for real-time interaction.
Implement monitoring and security best practices in cloud environments.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to deepen their understanding of conversational agents and apply LangChain to real-world use cases.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its application in building conversational agents.
Develop and deploy conversational agents using LangChain.
Integrate conversational agents with APIs and external services.
Apply Natural Language Processing (NLP) techniques to improve the performance of conversational agents.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.
By the end of this training, participants will be able to:
Set up an efficient environment for fine-tuning AI models on Ollama.
Prepare datasets for supervised fine-tuning and reinforcement learning.
Optimize AI models for performance, accuracy, and efficiency.
Deploy customized models in production environments.
Evaluate model improvements and ensure robustness.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.
By the end of this training, participants will be able to:
Explain what generative AI is and how it works.
Describe the transformer architecture that powers LLMs.
Use empirical scaling laws to optimize LLMs for different tasks and constraints.
Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
Discuss the opportunities and risks of generative AI for society and business.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at developers and IT professionals of all skill levels who wish to automate tasks and processes using AI without writing extensive code.
By the end of this training, participants will be able to:
Design and implement complex workflows using n8n's visual programming interface.
Integrate AI capabilities into workflows using LangChain.
Build custom chatbots and virtual assistants for various use cases.
Perform advanced data analysis and processing with AI agents.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.
By the end of this training, participants will be able to:
Deploy and configure Ollama for private AI processing.
Integrate AI models into secure enterprise workflows.
Optimize AI performance while maintaining data privacy.
Automate business processes with on-premise AI capabilities.
Ensure compliance with enterprise security and governance policies.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
Set up and deploy LLMs using Ollama.
Optimize AI models for performance and efficiency.
Leverage GPU acceleration for improved inference speeds.
Integrate Ollama into workflows and applications.
Monitor and maintain AI model performance over time.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to customize pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
Understand the principles of fine-tuning and its applications.
Prepare datasets for fine-tuning pre-trained models.
Fine-tune large language models (LLMs) for NLP tasks.
Optimize model performance and address common challenges.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.
By the end of this training, participants will be able to:
Grasp the fundamental principles of LangChain.
Set up and configure the LangChain environment.
Understand the architecture and how LangChain interacts with large language models (LLMs).
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.
By the end of this training, participants will be able to:
Establish a development environment with LLMs and essential tools.
Expertly perform NLU and NLI tasks with LLMs.
Extract, infer, and utilize knowledge graphs effectively.
Generate and manage dialogues using LLMs for conversational applications.
Evaluate content quality and diversity generated by LLMs and generative AI.
Apply ethical principles, ensuring fairness and responsible use of LLMs.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its components.
Integrate LangChain with large language models (LLMs) like GPT-4.
Build modular AI applications using LangChain.
Troubleshoot common issues in LangChain applications.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.
By the end of this training, participants will be able to:
Set up a development environment that includes a popular LLM.
Create a basic LLM and fine-tune it on a custom dataset.
Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.
By the end of this training, participants will be able to:
Understand the fundamentals of Ollama and its capabilities.
Set up Ollama for running local AI models.
Deploy and interact with LLMs using Ollama.
Optimize performance and resource usage for AI workloads.
Explore use cases for local AI deployment in various industries.
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Testimonials (1)
prompts engineering part
Michal - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
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