AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment – MIT

Artificial Intelligence (AI) is a powerful tool—but without a comprehensive and integrated framework supporting your initiatives, your organization may miss out on significant value.

AI continues to revolutionize industries such as energy, consumer products and services, automotive, financial services, national security, healthcare, and advertising. Yet business and IT leaders often have a limited view, focusing only on machine learning. In reality, AI technologies are fundamental components of complex systems that require reliable data, proper processing, responsible governance frameworks, and a balanced interaction between humans and machines.

This program equips professionals to lead, develop, and deploy responsible AI systems that augment human capabilities, using a holistic perspective focused on systems engineering. Participants gain practical tools to transition AI initiatives from development to production.

This five-day intensive program provides the skills and strategies needed to adopt a systems engineering approach to AI that maximizes the value of your digital products and services. Through interactive exercises, industry speakers, and a practical team project, participants will:

• Understand the appropriate roles for humans versus machines.
• Develop skills to lead and manage advanced technical teams.
• Gain hands-on experience building machine learning models using Jupyter Notebook.
• Master the ten key principles for integrating people, processes, and technology for successful AI deployment.
• Learn the role of GPUs and TPUs in efficiently running machine learning algorithms.
• Engage with AI practitioners sharing real-world experiences, emphasizing a holistic approach from architecture to deployment.
• Receive a brief introduction to Generative AI, with a focus on Large Language Models (LLMs).

• Understand the end-to-end AI architecture from a systems engineering perspective.
• Master the components of the AI pipeline.
• Engage in hands-on exercises and review key research papers in the field.
• Formulate and communicate the business value of AI to stakeholders.
• Integrate the concept of Responsible AI (RAI) from the early stages of development.
• Acquire practical guidance for deploying AI capabilities, emphasizing DevOps, MLOps, and DevSecOps.
• Learn from AI practitioners across multiple industries.
• Develop a strategic vision and roadmap for AI products and services, including Generative AI.