1 Getting started with MLOps and ML engineering

This chapter sets the stage for building reliable, production-grade ML systems by focusing on the operational challenges where most projects stumble—data quality, automation, deployment, and maintenance—rather than on model complexity. It introduces a practical, hands-on path to becoming a confident ML engineer, emphasizing real-world patterns, reproducibility, and scalable architectures. The approach is iterative, grounded in end-to-end thinking from problem framing through deployment and monitoring, with an emphasis on learning by doing.

The ML life cycle is presented in two broad phases. During experimentation, teams iterate through problem formulation, data collection and preparation, data versioning, model training, evaluation, and stakeholder validation—ideally orchestrated as reproducible pipelines. In the dev/staging/production phase, those pipelines are fully automated and triggered via CI or programmatic events to support deployment, versioned releases, and continuous monitoring. Production concerns include containerization and scalability, performance and reliability testing, monitoring infrastructure and business metrics alongside data/model drift, and automated retraining based on schedules or thresholds.

Success in MLOps draws on strong software engineering foundations, practical ML and data engineering skills, and a bias toward automation for repeatability and auditability. The chapter advocates incrementally building an ML platform—centered on Kubernetes and Kubeflow Pipelines—then extending it with feature stores, model registries, and CI/CD-driven deployment. Tool choices are pragmatic and context-dependent, with a “build vs buy” lens informed by first-hand platform assembly. The roadmap applies these principles through three projects: an OCR service, a tabular movie recommender (highlighting feature stores, drift, and observability), and a RAG-based documentation assistant that extends the same foundation to LLMOps.

The experimentation phase of the ML life cycle

The dev/staging/production phase of the ML life cycle

MLOps is a mix of different skill sets

The mental map of an ML setup, detailing the project flow from planning to deployment and the tools typically involved in the process

Traditional MLOps (right) extended with LLMOps components (left) for production LLM systems. Chapters 12-13 explore these extensions in detail.

An automated pipeline being executed in Kubeflow.

Feature Stores take in transformed data (features) as input, and have facilities to store, catalog, and serve features.

The model registry captures metadata, parameters, artifacts, and the ML model and in turn exposes a model endpoint.

Model deployment consists of the container registry, CI/CD, and automation working in concert to deploy ML services.

• The Machine Learning (ML) life cycle provides a framework for confidently taking ML projects from idea to production. While iterative in nature, understanding each phase helps you navigate the complexities of ML development.
• Building reliable ML systems requires a combination of skills spanning software engineering, MLOps, and data science. Rather than trying to master everything at once, focus on understanding how these skills work together to create robust ML systems.
• A well-designed ML Platform forms the foundation for confidently developing and deploying ML services. We'll use tools like Kubeflow Pipelines for automation, MLFlow for model management, and Feast for feature management - learning how to integrate them effectively for production use.
• We'll apply these concepts by building two different types of ML systems: an OCR system and a Movie recommender. Through these projects, you'll gain hands-on experience with both image and tabular data, building confidence in handling diverse ML challenges.
• Traditional MLOps principles extend naturally to Large Language Models through LLMOps - adding components for document processing, retrieval systems, and specialized monitoring. Understanding this evolution prepares you for the modern ML landscape.
• The first step is to identify the problem the ML model is going to solve, followed by collecting and preparing the data to train and evaluate the model. Data versioning enables reproducibility, and model training is automated using a pipeline.
• The ML life cycle serves as our guide throughout the book, helping us understand not just how to build models, but how to create reliable, production-ready ML systems that deliver real business value.

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