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

AI is revolutionizing many industries, including energy, consumer products and services, automotive, financial services, national security, healthcare, and advertising. But too often, business and IT leaders take a limited view of AI, focusing almost exclusively on machine learning (ML) methods. But AI technologies are, in fact, key enablers to complex systems. They require not only ML technologies, but also trustworthy data sensors and sources, appropriate data conditioning processes, responsible governance frameworks, and a balance between human and machine interactions. In short, organizations must evolve into a systems engineering mindset to optimize their AI investments.

This program will equip professionals to lead, develop, and deploy AI systems in responsible ways that augment human capabilities. Taking a broader, holistic perspective, it emphasizes an AI system architecture approach applied to products and services and provides techniques for transitioning from development into operations. To get the most of your AI initiatives, you must consider the entire ecosystem surrounding your AI systems and then recruit and retain talented multi-disciplinary teams to be successful.

Over five days, you will examine the trade-offs between roles best suited to humans vs. machines and develop the skills you need to lead and manage high-technology teams. Through interactive exercises and lectures, you will acquire practical experience building ML models using Jupyter Notebook, and master 10 principles for incorporating people, processes, and technologies in the successful deployment of AI products and/or services. You will also explore what makes GPUs and TPUs well-matched to executing machine learning algorithms.

Unique to this course, we'll host a panel of AI Experts so that participants will be able to interact in person during a luncheon after the panel session. The panel will focus the discussion on Generative AI (e.g., ChatGPT) and its responsible development and deployment. 

Upon completion of this program, you will have the skills to understand the AI fundamentals necessary to develop end-to-end systems, lead AI teams, and successfully deploy AI capabilities.

Participants will receive an early electronic publication of our upcoming book titled - Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment published by the MIT Press. 

NOTE: This is not a programming course. While coding is touched on, it is not a significant component of the curriculum.

COVID-19 Updates: MIT Professional Education fully expects to hold courses on-campus during the Summer of 2023 (only a handful will be delivered in the live virtual or hybrid modality). In the event there is a change in MIT's COVID-19 policies and a course cannot be held on-campus, we will deliver courses via live virtual format. Find the latest information here.

• Understand an end-to-end AI architecture at the systems engineering level
• Master the AI pipeline building blocks
• Experiment via AI exercises and review seminal AI papers
• Communicate your value proposition to stakeholders
• Learn to incorporate Responsible AI (RAI) from the beginning of the development cycle
• Acquire guidelines for successfully deploying AI system capabilities, with emphasis on DevOps, MLOps, and DevSecOps
• Receive practical experience from the “voice of AI practitioners” across various industries
• Formulate a strategic vision and development plan focused on AI products and services

Program Outline

This course runs 8:45 am - 5:00 pm each day except for the last day when it ends at 3:00 pm. 

DAY ONE

• Course Introduction
• Lecture 1: AI background and building blocks
• Lecture 2: AI strategic vision and project roadmap
• Fireside Chat
• Lecture 3: Data conditioning
• Team Proposals
• Team Presentations

DAY TWO

• Lecture 4: Machine learning
• Hands-on Exercise: Multi-Layer Perceptron (MLP) machine learning model
• Lecture 4 Takeaways
• Lecture 5: Modern computing
• Team Project

DAY THREE

• Lecture 6: Human-machine teaming
• Lecture 7: Responsible AI
• Fireside Chat
• Lecture 8: Guidelines for AI deployment
• Team Project

DAY FOUR

• Lecture 9: Development, security, and operations
• Lecture 10: Fostering an innovative team environment
• Fireside Chat
• Lecture 11: Leadership and resilience
• Team Project

DAY FIVE

• Lecture 12: Putting it all together
• Team Presentations
• Course Wrap-Up
   

This program is ideal for professionals who are responsible for the successful deployment of AI capabilities and technologies. Participants are not required to have a deep technical background, but having some familiarity with AI concepts will be helpful. Within this range, roles that will benefit from the course curriculum include, but are not limited to:

• Systems engineers who need practical frameworks for building AI pipelines and launching new projects
• Executives looking to manage change and make smart investment decisions related to AI technology
• Technical leaders who require specialized knowledge to head effective AI-powered teams
• Product directors who want to develop cohesive plans for product development and deployment
• Team leaders who guide high-technology groups in executing complex AI initiatives
• Researchers who want to explore and advance AI applications across industries
• Entrepreneurs who need actionable roadmaps for building and growing AI-powered businesses