The AI Optimization Playbook

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

16 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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This practical guide empowers AI and tech leaders to bridge the business–technology divide by optimizing the full AI lifecycle, from strategy and prototyping to scaling and governance, using proven frameworks and enterprise case studies. This resource equips leaders with the tools to align AI initiatives with business strategy, ensuring impactful and responsible implementation. It provides actionable guidance on scaling AI solutions, integrating ethical practices, and driving measurable outcomes. Designed for professionals seeking to bridge the gap between technology and business, it offers insights from industry experts who have shaped AI strategies at scale. This resource is ideal for AI/ML leaders, CTOs, CIOs, CDAOs, and CAIOs who are responsible for driving AI innovation and operational efficiency. A foundational understanding of AI and enterprise technology is recommended to fully benefit from the content. Hands-on approach towards optimizing your AI workflows for business use-cases This course is based on The AI Optimization Playbook, by Dr. Chun Schiros, Supreet Kaur, Rajdeep Arora and Dr. Usha Jagannathan. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Understanding the Perils of AI Products
- This module explores the key challenges that lead to AI project failures, including siloed development, non-deterministic behavior, and lack of production readiness. Learners will gain insight into how to align AI strategies with business goals and ensure scalable, reliable AI implementations.
Building the Enterprise AI Strategy
- This module equips learners with the knowledge to develop and implement a robust enterprise AI strategy, focusing on aligning AI initiatives with business goals, ensuring data governance, and building scalable AI infrastructure. It covers the challenges of AI adoption, including regulatory compliance, data strategy, and organizational change management.
Selecting High-Impact AI Projects
- This module guides learners through the process of identifying and selecting AI projects with the greatest potential for business impact. It covers evaluating feasibility, aligning AI initiatives with organizational goals, and analyzing risk and opportunity. Learners will gain practical tools to make informed decisions about AI implementation.
Beyond the Build: Gaining Leadership Support for AI Initiatives
- This module equips learners with strategies to secure leadership support for AI initiatives by aligning AI goals with business strategies, crafting compelling narratives, and using real-world examples to build confidence in AI projects.
Building an AI Proof of Concept and Measuring Your Solution
- This module explores the process of creating and evaluating AI Proof of Concept (PoC) projects, focusing on strategic decision-making, performance measurement, and risk management. Learners will gain insights into best practices for AI implementation and how to transition from pilot projects to full-scale solutions. The content emphasizes practical steps for validating AI ideas and ensuring trust in AI systems.
Beyond Accuracy: A Guide to Defining Metrics for Adoption
- This module covers how to define effective metrics for AI/ML models, including balancing trade-offs in multi-objective optimization and understanding the impact of operational latency on system performance. Learners will gain insights into aligning technical and business goals through structured metric frameworks.
From Model to Market: Operationalizing ML Systems
- This module covers the process of moving machine learning models from experimentation to production, focusing on productization, pipeline development, and the importance of reproducibility and continuous improvement in real-world applications.
From Metrics to Measurement: Experimentation and Causal Inference
- This module delves into the challenges of measuring the impact of machine learning systems through causal inference. It covers experimental and observational methods, including A/B testing and quasi-experimental techniques, to help learners understand how to assess real-world outcomes using historical data.
Generative AI in the Enterprise: Unlocking New Opportunities
- This module explores the practical application of generative AI in enterprise settings, covering when and how to implement GenAI, measuring its business value, and building real-world scenarios like data chatbots. Learners will gain insights into leveraging AI for productivity, cost savings, and strategic decision-making.
Understanding GenAI Operations
- This module provides an in-depth look at Generative AI Operations, covering the evolution of large language models, the life cycle of GenAI systems, and best practices for development and evaluation. Learners will explore real-world case studies that highlight how enterprises implement and manage AI solutions effectively.
AI Agents Explained
- This module explores the concept of AI agents, their applications in real-world scenarios, and the frameworks used to build and manage them. Learners will gain insights into when to use AI agents, how to implement observability, and best practices for enterprise-level deployment.
Introduction to Responsible AI
- This module explores the foundational principles of Responsible AI, including its role in ethical business practices, the importance of fairness, transparency, and accountability, and how organizations can build trust through collaborative RAI efforts. Learners will gain insights into real-world applications and the responsibilities involved in developing ethical AI systems.
Implementing RAI Frameworks, Metrics, and Best Practices
- This module provides practical strategies for embedding ethical AI practices through governance frameworks, risk assessment, and regulatory compliance. Learners will gain insights into defining and monitoring RAI metrics, as well as integrating RAI into organizational culture through training and leadership buy-in.
Building Trustworthy LLMs and Generative AI
- This module explores the ethical and technical challenges of building trustworthy large language models and generative AI systems. Learners will gain an understanding of bias mitigation, fairness, and data privacy strategies to ensure responsible AI deployment in real-world applications.
Regulatory and Legal Frameworks for Responsible AI
- This module provides an in-depth look at global AI regulatory frameworks, focusing on compliance strategies, risk management, and the ethical implications of AI deployment. Learners will gain insights into navigating cross-border AI regulations, implementing KYAI compliance, and managing liability in the age of generative AI.
The Future of AI Optimization: Trends, Vision, and Responsible Implementation
- This module explores emerging trends in AI optimization, responsible implementation strategies, and the societal impact of AI, preparing learners to understand and navigate the ethical, technical, and sustainable challenges of AI development through 2030.