Course is closed
Lead Instructor(s)
Oct 17 - 21, 2022
Registration Deadline
Live Virtual
Course Length
5 days
Course Fee
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Artificial intelligence (AI) is a powerful tool—but without the right system-wide architecture in place to support your initiatives, your organization is leaving value on the table. Featuring interactive exercises, industry speakers, and a hands-on group project, this dynamic five-day course is designed to equip you with the skills and strategies you need to deploy an AI systems engineering approach that maximizes the value of your digital products and services.

This course may be taken as a standalone program or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence. COMPLETING THE COURSE WILL CONTRIBUTE 5 DAYS TOWARDS THE CERTIFICATE.

Course Overview

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, you’ll attend live online fireside chats to discover how four accomplished professionals are putting the power of AI to work in their respective fields. 

Meet this year’s fireside speakers

  • Dr. Christy Fernandez-Cull, CEO and Founder, Da Vinci Wearables
    An AI technologist, Dr. Cull’s research focuses on sensor design and optimization. She is the CEO and Founder of DaVinci Wearables, where her team is developing smart undergarments that use data to help people make better health and lifestyle choices. Prior to founding her company, she was the Head of Sensors at the Lyft Level 5 Self-Driving Division, where she leveraged sensor architecture to optimize on-demand transportation.
  • Dr. Stefanie Tignor, Head of Data Science and Insights, Humu
    At Humu, Dr. Tignor makes analytics-informed recommendations for organizations and uses data to model how individuals can thrive at work. Prior to joining the organization, she was a professor at Northeastern University, where she conducted and published research on the use of data science to better understand human personalities and wellbeing.
  • Udgam Goyal, Senior Product Manager, Aurora
    As Senior Product Manager at Aurora, Udgam Goyal oversees the development of core motion planning and perception simulation products for autonomous vehicles. Prior to working at Aurora, Udgam conducted research at MIT CSAIL and the MIT Media Lab, where he developed path optimization algorithms for self-driving vehicles in dense urban environments.
  • Jessica Larson, Data Engineer, Pinterest
    An engineer within the Enterprise Data Warehouse team at Pinterest, Jessica Larson has spent her career working in data governance, developing and maintaining data pipelines, and working with security and privacy teams to keep organizations’ information airtight. Additionally, serves as a mentor to budding technical professionals, designing new-user onboarding processes and creating documentation to help colleagues at all technical levels utilize data visualization software. 

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.

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

Learning Outcomes
  • 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. 


  • 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


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


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


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


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

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


“David Martinez and Bruke Kifle helped lay key foundations for our organization’s AI strategy and approach.”
Michael Moulin-Ramsden, Head of New Ventures, Veolia Nuclear Solutions
“The course was a truly unique experience. I’ve worked with AI for several years, but the faculty provided a comprehensive picture of the field that I haven‘t seen anywhere else.”
Rainer Hoffmann, Senior Manager of Digital Transformation, EnBW Energie Baden-Württemberg AG
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