> 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/literature.md). # MLOps Literature Operations work has a shelf of books behind it, and reading them in order turns a pile of tools into a way of thinking. The list starts with the twelve-factor app, then data-intensive systems, SRE, and distributed systems, moves to Jez Humble's delivery books, and ends with code craft: clean code, domain-driven design, a Python course, and a talk archive. ## Twelve-factor The first idea is how a single service should be built so that it can be operated at all. [12 Factors](https://12factor.net/). The Twelve-Factor App is a methodology for building scalable and portable SaaS applications. It emphasizes best practices like maintaining a single codebase, isolating dependencies, storing configuration in the environment, treating services as replaceable resources, and enabling fast, stateless, and resilient deployments across consistent environments. ## Data-intensive applications Once one service is well built, the hard part moves to the data it reads and writes. [Designing Data-Intensive Applications: reliable, maintainable](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) is the book, and the [Medium](https://medium.com/@m_mcclarty/tech-book-talk-designing-data-intensive-applications-eb4908f2f6d6) post is a Tech Book Talk review of it, part of a monthly series reviewing a technical book finished in the last month. ## SRE Data systems that people depend on have to stay up, which is what site reliability engineering is about. [Google SRE](https://landing.google.com/sre/books/) is Google's page for its SRE books, a guide to SRE principles, best practices, and the role of a reliability engineer. The two books behind it are [Site Reliability Engineering](https://www.amazon.com/dp/149192912X?psc=1\&pf_rd_p=0c07d3ef-dd9a-4ce4-8daa-7b9b90db3048\&pf_rd_r=F4QGSBSPA6DJJCX10WXK\&pd_rd_wg=RfKM8\&pd_rd_i=149192912X\&pd_rd_w=khBYM\&pd_rd_r=5bd80e38-7a30-41bb-ae0e-25a91dd1cb3d\&ref_=pd_luc_rh_crh_rh_sbs_sem_01_03_t_ttl_lh), the principles, and the [Site Reliability Workbook](https://www.amazon.com/Site-Reliability-Workbook-Practical-Implement/dp/1492029505/ref=sr_1_1?dchild=1\&keywords=The+Site+Reliability+Workbook\&link_code=qs\&qid=1598257953\&sr=8-1\&tag=amznsearchff-20), the practical companion. ## Distributed systems and microservices Reliability gets harder when the system is split across many machines and services. [Designing Distributed Systems: patterns and paradigms](https://www.amazon.com/Designing-Distributed-Systems-Patterns-Paradigms/dp/1491983647) is the book of distributed-system patterns, and [Building Microservices: Designing Fine-Grained Systems](https://www.amazon.com/dp/1491950358/?coliid=I1H3OSVXC7XRBL\&colid=300M9JC4311P3\&psc=0\&ref_=lv_ov_lig_dp_it) is the book on cutting a system into fine-grained services. ## Jez Humble With the architecture in place, the next question is how changes get delivered through it, and Jez Humble's books are the delivery shelf. [Jez Humble](https://www.amazon.com/Jez-Humble/e/B003SNGS8E/ref=dp_byline_cont_pop_book_2) is his Amazon author page with the full bibliography. From it, [Accelerate: software and high-performing organizations](https://www.amazon.com/Accelerate-Software-Performing-Technology-Organizations/dp/1942788339/ref=tmm_pap_swatch_0?_encoding=UTF8\&qid=\&sr=) is about software and high-performing organizations, the [DevOps Handbook](https://www.amazon.com/DevOps-Handbook-World-Class-Reliability-Organizations/dp/1942788002/ref=tmm_pap_swatch_0?_encoding=UTF8\&qid=\&sr=) is the DevOps book, and [Continuous Delivery: deployment](https://www.amazon.com/Continuous-Delivery-Deployment-Automation-Addison-Wesley-dp-0321601912/dp/0321601912/ref=mt_other?_encoding=UTF8\&me=\&qid=) is the deployment book. Lean Enterprise completes the list, without a link. ## Clean code Delivery pipelines only help if the code moving through them is readable. [Clean Code](https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882) (Java). The first chapters are good. For Python, [Clean Code in Python](https://www.packtpub.com/product/clean-code-in-python/9781788835831) starts from the point that there is no strict definition or formal measure of clean code: checkers, linters, and static analyzers are necessary, but not sufficient. The [Medium](https://medium.com/@m_mcclarty/tech-book-talk-clean-code-in-python-aa2c92c6564f) post is a book talk on that same Python book, and the [Git](https://github.com/zedr/clean-code-python) repo is zedr's :bathtub: Clean Code concepts adapted for Python. ## Domain-driven design Clean functions still need a model of the business they serve. [Domain-Driven Design](https://www.amazon.com/Domain-Driven-Design-Reference-Definitions-Summaries/dp/1457501198/ref=pd_cart_crc_cko_mrai_1_1/146-7136232-7217867?_encoding=UTF8\&pd_rd_i=1457501198\&pd_rd_r=e0b19e54-c0c3-4ad1-abd0-eff99f815aee\&pd_rd_w=TP8KJ\&pd_rd_wg=ompBT\&pf_rd_p=77f3805b-bff9-40ee-9688-bcdb2cd9e197\&pf_rd_r=1WF281MJHVQYG506PTRX\&psc=1\&refRID=1WF281MJHVQYG506PTRX) is the reference, with its definitions and summaries. ## The Python course For the language underneath most of this work, a single video course covers it end to end. [The Complete Python Course](https://www.packtpub.com/product/the-complete-python-course-video/9781839217289) is that video course. ## pyVideo After the course, talks keep the learning going. [pyVideo](https://pyvideo.org/) is PyVideo.org, the archive of Python talks. --- # 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. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/ai-engineering/literature.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.