AI Engineering Training in Dartmouth

This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level AI professionals and software developers who wish to build intelligent applications using AI and ML.

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

• Understand the advanced concepts and technologies behind AI and ML.
• Analyze and visualize data to inform AI/ML model development.
• Build, train, and deploy AI/ML models effectively.
• Create intelligent applications that can solve real-world problems.
• Evaluate the ethical implications of AI applications in various industries.

This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level data scientists, engineers, and AI practitioners who wish to master the intricacies of intelligent applications and leverage them to solve complex, real-world problems.

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

• Implement and analyze deep learning architectures.
• Apply machine learning at scale in a distributed computing environment.
• Design and execute reinforcement learning models for decision-making.
• Develop sophisticated NLP systems for language understanding.
• Utilize computer vision techniques for image and video analysis.
• Address ethical considerations in the development and deployment of AI systems.

This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level AI engineers and software developers who wish to gain a foundational understanding of AI engineering principles and practices.

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

• Understand the core concepts and technologies behind AI and machine learning.
• Implement basic machine learning models using TensorFlow and PyTorch.
• Apply AI techniques to solve practical problems in software development.
• Manage and maintain AI projects using best practices in AI engineering.
• Recognize the ethical implications and responsibilities involved in developing AI systems.

This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level IT professionals who wish to gain a foundational understanding of intelligent applications and how they can be applied in various industries.

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

• Understand the history, principles, and impact of artificial intelligence.
• Identify and apply different machine learning algorithms.
• Manage and analyze data effectively for AI applications.
• Recognize the practical applications and limitations of AI in different sectors.
• Discuss the ethical considerations and societal implications of AI technology.

This two-day, hands-on program equips participants with practical skills to use Generative AI tools effectively, safely, and consistently in technical and operational work. The training focuses on turning AI into a reliable day-to-day assistant for drafting and improving documentation, structuring requirements, accelerating analysis and reporting, supporting problem solving, and streamlining cross-functional communication. Participants learn proven prompting patterns, validation techniques to reduce errors and “hallucinations,” and lightweight governance practices to protect confidential information and intellectual property. The course is designed to produce immediately usable templates, workflows, and a repeatable approach to AI-assisted work.

AI is rapidly becoming the foundation of modern product management — transforming how we discover customer needs, define solutions, and make decisions. Just as electricity revolutionized industry, Artificial Intelligence is reshaping how products are built, validated, and evolved.

This instructor-led, live training (online or onsite) is aimed at Product Owners, Scrum Masters, and Agile Coaches who want to understand how AI can enhance product ownership and strengthen decision-making.

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

• Understand the fundamentals of Artificial Intelligence and differentiate between traditional AI and Generative AI (GenAI).
• Apply the seven preferred stances of a Product Owner (including the new Orchestrator stance) and use AI to strengthen each stance — such as Visionary, Experimenter, or Customer Representative.
• Master effective prompting and treat AI tools as intelligent collaborators with specialized skills.
• Deepen customer understanding and create AI-supported personas for hypothesis testing and discovery, while maintaining authentic customer contact.
• Develop and communicate a clear product vision using structured frameworks like the 3x3 Framework, leveraging AI to shape and visualize narratives.
• Use Generative AI tools to accelerate prototyping and hypothesis testing, such as generating mock-ups or product concepts.
• Support product decisions by using AI tools to analyze, prioritize, and evaluate features based on value, feasibility, and user impact.
• Automate or delegate routine daily tasks (emails, stakeholder management, note-taking) using AI to focus on strategic and creative work.
• Lead discussions on AI ethics, bias, and data security, ensuring responsible and sustainable AI adoption.
• Identify and design valuable AI use cases tailored to the specific product and organizational context.
• Explore and experiment with around 20 different AI tools and models — from productivity assistants and prototyping platforms to generative video, image, and coding solutions.