chapter one
1 Getting started with MLOps and ML engineering
This chapter covers
- Understanding Machine Learning systems in production
- The complete ML life cycle from experimentation to deployment
- Essential skills for production Machine Learning engineering
- Building your first Machine Learning platform
- Real-world ML project architectures
Are you ready to build production-grade Machine Learning (ML) systems with confidence? While many resources teach you how to build models, few show you how to successfully deploy and maintain them in production. Machine Learning Operations (MLOps) remains a challenging field where most projects fail not due to model complexity, but because of the intricacies of building reliable, scalable ML systems.
This book will transform you into a confident ML Engineer - someone who can successfully shepherd ML projects from conception to production. Through hands-on examples and real-world scenarios, you'll learn:
- How to design and implement reliable ML systems that work in production
- The complete ML life cycle, from problem formulation to monitoring
- Essential patterns for building robust ML pipelines and services
- Practical MLOps skills that companies actually need
- Real-world techniques for maintaining ML systems at scale