Value from big data becomes real when your data-intensive AI, machine learning and analytics applications go into production. That can be challenging, but this non-technical but practical guide helps by showing you what has made others successful.
Approaches such as Kubernetes for containerization, an effective data platform, streaming microservices and multi-tenancy can help you deploy across multi-cloud, hybrid cloud, and IoT edge computing. Whether you already have data-driven applications in production, are about to deploy, or are just getting started, you'll find something helpful in this book.
Download this book to:
Recognize goals, challenges, and potential pitfalls of deploying Ai and analytics applications to production
Learn how best to plan, design, and execute large data systems tied to practical business considerations
Develop a production-ready culture across your organization
Focus on the special case of machine learning and AI in production
Examine MapR, a data platform with the technical capabilities to support emerging trends for large-scale data
Explore a range of design patterns that work well for production customers in multiple industries