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

January 31, 2022 - February 4, 2022
AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment
MIT Professional Education


Location

Live Virtual

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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 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.

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.

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.

 

For more information, please visit the MIT Professional Education course listing here.

  • 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 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.

    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.

    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.

     

    For more information, please visit the MIT Professional Education course listing here.

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