Fundamentals of Operationalizing AI
94-879
Units: 6
Description
Artificial Intelligence (AI) is rapidly transforming industries by driving innovation, improving efficiency, and enhancing decision-making processes. Yet, despite its potential, many AI projects face significant hurdles in deployment. According to a recent survey, only 22% of data scientists report that their "revolutionary" AI initiatives—those designed to enable new processes or capabilities—usually reach deployment. Alarmingly, 43% say that 80% or more of their AI projects fail to make it into production. Even when considering all types of machine learning projects, including those focused on refreshing existing models, only 32% of models typically deploy. These statistics underscore the critical challenges of scaling AI systems effectively.
This course is designed to tackle these challenges head-on, providing graduate-level students with a comprehensive understanding of the AI lifecycle. Students will learn how to navigate the complex process of identifying which business tasks should be automated through AI and which decisions should be augmented using AI. The course introduces practical frameworks essential for making these strategic decisions and successfully implementing AI solutions.
Throughout the course, students will engage deeply with each stage of the AI lifecycle. They will learn to identify and prioritize high-impact AI use cases, conduct thorough cost-benefit analyses, and design strategic roadmaps aimed at maximizing return on investment (ROI). The curriculum blends theoretical knowledge with hands-on experience using industry-standard tools such as Jupyter Lab, Docker, Kubernetes, Kafka, and Evidently. These tools are critical for overcoming the common pitfalls associated with AI deployment and preparing students to scale AI systems in real-world environments.
The course’s practical orientation is further enhanced through case studies that serve as a foundation for class discussions. These case studies provide students with the opportunity to analyze real-world AI applications, assess the challenges involved, and understand the decision-making processes behind successful implementations. Additionally, two guest lectures from seasoned industry practitioners will offer firsthand insights into the practical challenges of AI deployment across various sectors.
A strong emphasis is placed on governance and trust, equipping students with the knowledge to develop ethical, transparent, and effective AI systems. Students will learn how to integrate AI into organizational processes, assess talent and skill gaps, and create strategies to build the necessary capabilities for sustained AI-driven innovation.
This course is essential for students aspiring to careers as AI engineers, AI analysts, or AI governance experts. It is equally invaluable for business and technology students who wish to understand how to manage the development and deployment of AI systems. By the end of the course, students will possess a well-rounded, practical understanding of AI system management, enabling them to lead AI-driven projects and drive innovation across industries.
Learning Outcomes
- AI Lifecycle Mastery: Develop a thorough understanding of the AI system lifecycle, including the identification of business tasks for automation or augmentation, and effectively manage data preparation, model development, deployment, and maintenance to ensure alignment with organizational goals and industry best practices.
- Strategic Value Realization: Identify and assess business needs, conduct detailed cost-benefit analyses, and design strategic roadmaps that prioritize high-impact AI initiatives. Develop AI models that deliver maximum ROI and are closely aligned with business objectives.
- Effective AI Operationalization: Design and implement strategies to successfully deploy, monitor, and maintain AI models at scale, with a focus on overcoming common deployment challenges, ensuring system reliability, building trust, and fostering continuous improvement.
- Ethical Governance and Risk Management: Establish and enforce comprehensive governance frameworks that promote ethical, transparent, and effective AI operations. Address potential risks proactively while fostering stakeholder trust and confidence in AI systems.
- Organizational Integration and Skill Building: Evaluate organizational talent and process needs, and create actionable strategic plans to address skill gaps. Integrate AI seamlessly into business processes and build the necessary capabilities to sustain AI-driven innovation within the organization.
- Practical Tool Proficiency: Gain hands-on experience with key AI lifecycle tools—Jupyter Lab, Docker, Kubernetes, Kubeflow, Kafka, and Evidently—preparing you to effectively scale and deploy AI systems in real-world environments.
Prerequisites Description
Basic understanding of statistics and machine learning is useful, but not necessary.