Build and deploy enterprise-grade multimodal AI systems using generative models for text, image, audio, and video.
Key Features
- Design end-to-end multimodal AI systems for enterprise workloads
- Build scalable pipelines using Gemini, Veo, Imagen, Airflow, and Kubeflow
- Implement governance, safety, and compliance for AI-generated content
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
The Multimodal AI Playbook equips enterprise developers and ML engineers with the practical skills needed to build real-world generative systems using text, image, audio, and video models. This book offers a complete stack approach—starting from theory and progressing through tooling, orchestration, governance, and full-scale deployment.
Beginning with the foundations of multimodal learning and generative AI models, you’ll explore how to generate modality-specific content while navigating key trade-offs in model selection, scalability, and orchestration. With a dedicated focus on enterprise use cases like automated marketing campaigns, the book shows how to manage model outputs responsibly covering bias detection, IP risk, and content safety.
You'll implement a full pipeline using enterprise-ready models such as Gemini, Veo, and Imagen. With forward-looking chapters on real-time generation, 3D assets, and multimodal agents, this book bridges the gap between technical mastery and business impact- preparing you to lead in the AI-first future.
What you will learn
- Design full-stack multimodal AI architectures
- Generate text, image, audio, and video content
- Build automated AI-powered marketing workflows
- Compare and fine-tune leading multimodal models
- Deploy AI pipelines with Airflow and Kubeflow
- Implement AI safety, governance, and compliance
- Create real-time and 3D multimodal experiences
- Integrate AI with enterprise cloud platforms
Who this book is for
Software engineers, ML engineers, data scientists, technical product managers, and solution architects looking to build and deploy multimodal generative AI systems in production. Professionals familiar with Python, machine learning concepts, and cloud platforms such as AWS, Azure, or GCP will gain practical guidance for implementing scalable, enterprise-ready AI solutions.
Table of Contents
- Introduction to Generative AI and Multimodality
- Enterprise Context and Case Study
- Preparing the Environment
- Text Generation
- Image Generation
- Audio Generation
- Video Generation
- Responsible AI: Ethics, Bias, and Safety
- Security in Multimodal Enterprise AI
- Measuring ROI and Driving Business Value
- Project Definition: Automated Marketing Asset Generation
- Testing, Deployment, and Iteration
- Building the Pipeline
Maximilian Tschochohei leads Google Cloud's Customer Engineering team for AI, where he helps enterprise customers design and deploy advanced AI solutions for marketing use cases. Prior to joining Google, he worked in strategy and technology consulting at Boston Consulting Group, advising organizations on digital transformation and emerging technologies.
Fabian Schenker is a Staff Software Engineer at Google where he serves as a GenAI Global Blackbelt, helping organizations design, deploy, and scale enterprise AI solutions. With expertise in generative AI, machine learning, natural language processing, and cloud technologies, he partners with global enterprises to transform complex business challenges into production-ready AI applications. He specializes in bridging cutting-edge AI research with practical implementation, enabling organizations to adopt and scale AI solutions that deliver measurable business value.
