The Complete and Up-to-Date Guide to Build, Develop and Scale Production Ready AI Systems
While AI headlines promise sentient chatbots and superhuman intelligence, the reality in most organizations is far less glamorous: stalled proofs of concept, brittle infrastructure, rising cloud costs, and a whole lot of “now what?” The AI Engineering Bible offers a grounded, practical roadmap for professionals who need to move from demos to deployment.
Written for engineers, architects, tech leads, and product owners, this book skips the hype and focuses on what really matters: building scalable, secure, production-ready AI systems. Think of it as DevOps meets MLOps, with a full-stack mindset.
Inside, you’ll follow the entire AI lifecycle, from the moment someone says, “Let’s add AI,” to long-term maintenance and iteration. That includes:
- Framing the problem by aligning business goals, ethical concerns, and technical requirements
- Building stable pipelines from raw data through model selection
- Deploying at scale using containers, APIs, CI/CD, and cloud infrastructure
- Scaling intelligently with distributed inference, load balancing, and resource efficiency
- Optimizing for cost, latency, accuracy, and maintainability
- Managing model drift, retraining cycles, user feedback, and governance
This is not a book that stops at training a model. It is a detailed guide packed with engineering wisdom, scalable design patterns, and hard-earned lessons from real-world systems.
Whether you’re delivering AI for internal platforms, customer-facing features, or mission-critical enterprise solutions, The AI Engineering Bible is your field manual. It’s written for people who need AI systems that work reliably, efficiently, and at scale.
No magic. No buzzwords. Just serious engineering for real-world AI.