Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization.
- Build products that leverage AI for the common good and commercial success
- Take macro data and use it to show your customers you’re a source of truth
- Best practices and common pitfalls that impact companies while developing AI product
Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed.
The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products.
You’ll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You’ll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you’ll stay ahead of the curve in the rapidly evolving field of AI and ML.
By the end of this book, you’ll have understood how to navigate the world of AI from a product perspective.
What you will learn
- Build AI products for the future using minimal resources
- Identify opportunities where AI can be leveraged to meet business needs
- Collaborate with cross-functional teams to develop and deploy AI products
- Analyze the benefits and costs of developing products using ML and DL
- Explore the role of ethics and responsibility in dealing with sensitive data
- Understand performance and efficacy across verticals