> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-engineering/intro.md). # Introduction ## AI Engineering The model is now a service, and the service has to be built, deployed, and monitored. After this chapter the reader can place feature stores, experiment tracking, deployment, and monitoring on the path from a trained model to a running system, and feature stores continue [Data Processing](/data/processing-intro.md), where raw values were scaled, transformed, imputed, and annotated into the shape a learner expects. ## Ops The pages above are about the method: what it is and how it works. The pages in this half are about how you build, ship, govern, and run the system. That work splits into five groups. [MLOps](/ai-engineering/mlops-intro.md) is the gap between ML in a notebook and ML in production. [DataOps](/data/engineering-intro/sql.md) starts from asking a warehouse a question in SQL. [DevOps](/ai-engineering/devops-strategy.md) opens with the strategy that keeps delivery speed from stalling. [DevSecOps](/ai-engineering/tbd.md) puts security inside the delivery loop, not after it. [Architecture](/ai-engineering/problems.md) begins with the signs that a system which looks distributed still behaves like one monolith. A makeover of these pages was done, and [How it was done](https://github.com/orico/www.mlcompendium.com/tree/main/appendix/ops-makeover.md) records the process. ### Model platform The model platform comes first, because everything else in this half serves a model that has to run. [Definitions](/ai-engineering/definitions.md) separates the Ops names that get mixed: DataOps, MLOps, GitOps, DevOps, and DevSecOps. [MLOps Intro](/ai-engineering/mlops-intro.md) lays out the building blocks and reading list for getting from notebook to production. [MLOps Patterns](/ai-engineering/mlops.md) collects the shapes production ML keeps repeating, then two end-to-end systems that use them. [MLOps Teams](/ai-engineering/mlops-teams.md) asks who owns that work: machine learning engineers, or a separate MLOps role. With the owners named, the pages follow the model's path. [Feature Stores & Feature Pipelines](/ai-engineering/feature-stores-and-feature-pipelines.md) is the pipeline that builds features and the store that serves the same features to training and serving. [ML Experiment Management](/ai-engineering/experiment-management.md) keeps track of runs that multiply faster than anyone can remember them. [Model Formats](/ai-engineering/model-formats.md) is how a trained model leaves the notebook in a form another application can read. [MLOps Deployment](/ai-engineering/mlops-deployment.md) puts that model somewhere that can serve it. [MLOps Tools](/ai-engineering/full-stack-and-ops.md) walks the many tools of the stack, from CI and Docker to Kubeflow, workflow runners, and serving. [ML Model Monitoring & Alerts](/ai-engineering/mlops-monitoring-and-alerts.md) keeps a shipped model from going blind. [AI As Data](/ai-engineering/ai-as-data.md) treats models as AI tables you query with SQL, and [ML Architecture](/ai-engineering/ml-architecture.md) closes the group with end-to-end architecture examples. ### Architecture and security Once the model runs, the system around it has to scale, stay software, and stay secure. [System Design](/ai-engineering/system-design.md) is system design interviews, scalability reading, and practice for a service that has to scale. [Development Concepts](/ai-engineering/development-concepts.md) is design patterns, dependency injection, and SOLID, because ML code still has to be software. [Problems](/ai-engineering/problems.md) lists the coupling symptoms of a distributed monolith. On the security side, [DevSecOps Definitions](/ai-engineering/tbd.md) gives three definitions of DevSecOps, [DevSecOps Tools](/ai-engineering/tools.md) points at Atlassian's list of the toolchain, and [DevSecOps Concepts](/ai-engineering/concepts.md) is the simplified information-security vocabulary to have clear before the tools. ### DevOps The last group is the delivery machinery that every service above depends on. [DevOps Strategy](/ai-engineering/devops-strategy.md) points at eight strategies and how to put them in place, and [DevOps Tools](/ai-engineering/full-stack-and-ops-1.md) is the section that holds the tools. [Tutorials](/ai-engineering/full-stack-and-ops-1/tutorials.md) are two end-to-end walkthroughs: A/B serving, and a training-and-serving stack. [Continuous Integration](/ai-engineering/full-stack-and-ops-1/continuous-integration.md) is how code lands continuously. [Docker](/ai-engineering/full-stack-and-ops-1/docker.md) makes a data-science environment run the same way everywhere, and [Kubernetes](/ai-engineering/full-stack-and-ops-1/kubernetes.md) orchestrates those containers. [Cloud Objects](/ai-engineering/full-stack-and-ops-1/cloud-objects.md) compares AWS Lambda with Fargate for serverless compute. [Key Value DB](/ai-engineering/full-stack-and-ops-1/key-value-db.md) covers RabbitMQ, ActiveMQ, Kafka, ksqlDB, and ZooKeeper for the messages and state services pass. [API Gateway](/ai-engineering/full-stack-and-ops-1/api-gateway.md) is the single front door for services. [Infrastructure As code](/ai-engineering/full-stack-and-ops-1/infrastructure-as-code.md) turns cloud resources into code with Terraform. [Logs](/ai-engineering/full-stack-and-ops-1/logs.md) is the trail production systems leave, [ELK](/ai-engineering/full-stack-and-ops-1/elk.md) is the stack to search and visualize it, and [SLO](/ai-engineering/full-stack-and-ops-1/slo.md) turns uptime promises into measurable objectives with Sloth. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. 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