Scaling Responsible Ai
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Scaling Responsible AI
Implement AI in your organization with confidence while mitigating risk with responsible, ethical guardrails Much like a baby tiger in the wild, artificial intelligence is almost irresistibly alluring. But, just as those tiger cubs inevitably grow up into formidable and fierce adults, the dangers and risks of AI make it a force unto itself. Useful and profitable, yes, but also inherently powerful and risky. In Scaling Responsible AI: From Enthusiasm to Execution, celebrated speaker, AI strategist, and tech visionary Noelle Russell delivers an exciting and fascinating new discussion of how to implement artificial intelligence responsibly, ethically, and profitably at your organization. Responsible AI promises immense opportunity, but unguided enthusiasm can unleash serious risks. Learn how to implement AI ethically and profitably at your company with Scaling Responsible AI. In this groundbreaking book, Noelle Russell reveals an executable framework to: Harness AI's full potential while safeguarding your firm's reputation Mitigate bias, accuracy, privacy, and cybersecurity risks from the start Make informed choices by seeing through the hype and identifying true AI value Develop an ethical AI culture across teams and leadership Scaling Responsible AI equips executives, managers, and board members with the knowledge and responsibility to make smart AI decisions. Avoid compliance disasters, brand damage, or wasted resources on AI that fails to deliver. Implement artificial intelligence that drives profits, innovation, and competitive edge—the responsible way.
Introducing MLOps
Author: Mark Treveil
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2020-11-30
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
AI Product Management: Scaling Impact on BigTech 2025
Author: Author:1- Divij Pasrija, Author:1- Prof (Dr) Sandeep Kumar
language: en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date:
PREFACE Artificial Intelligence (AI) has rapidly become one of the most transformative forces across industries, offering unprecedented opportunities to innovate, automate, and create more personalized experiences. The integration of AI into product management processes has emerged as a critical capability for tech companies striving to maintain competitive advantage and deliver impactful solutions at scale. “AI Product Management: Scaling Impact in Big Tech” is designed to bridge the knowledge gap between the complex world of AI and the strategic, executional demands of product management within large tech organizations. As AI continues to reshape how products are developed, launched, and scaled, it presents both exciting opportunities and considerable challenges. For product managers, the integration of AI into their product strategy demands a unique understanding of machine learning, data infrastructure, and AI-driven decision-making processes. This book offers readers a comprehensive guide to navigate the complexities of AI product development, focusing on the challenges that come with building, scaling, and managing AI products in the fast-paced world of Big Tech. We will delve into the intersection of AI technology and product management, exploring key concepts, strategies, and tools essential for successfully managing AI-driven products. Whether you are an aspiring product manager, a seasoned professional looking to enhance your AI knowledge, or a leader in an organization with a growing AI portfolio, this book aims to equip you with the practical insights and frameworks necessary to scale AI products effectively. Drawing on real-world examples, industry case studies, and expert insights, “AI Product Management: Scaling Impact in Big Tech” will help you understand the full product lifecycle in AI, from ideation and development to deployment and iteration. In addition, it emphasizes the importance of collaboration across cross-functional teams, the role of ethical considerations, and the ability to align AI innovation with overarching business goals. This book is a resource for those who wish to master the art and science of AI product management. By the end of this journey, readers will be well-prepared to navigate the challenges and seize the opportunities that come with managing impactful AI products at scale, driving innovation, and creating tangible value for organizations and society alike. Author