Merge pull request 'Implement date-based blog post naming convention and add new content' (#12) from develop into main

Reviewed-on: #12

README.md

@@ -15,6 +15,16 @@ All commands are run from the root of the project, from a terminal:
 | `npm run astro ...`       | Run CLI commands like `astro add`, `astro check` |
 | `npm run astro -- --help` | Get help using the Astro CLI                     |
 
+## Blog post filename convention
+
+Manage files in `src/blog/` with this format:
+
+`YYYY-MM-DD-your-post-slug.md`
+
+- Use lowercase letters, numbers, and hyphens in the slug.
+- Keep words separated by a single hyphen.
+- If two posts share the same date and slug, append `-2`, `-3`, etc.
+
 ## Dependency update guidance
 
 For security/deprecated remediation, allow breaking changes via `npm audit fix --force`, then verify:

src/blog/2026-02-16-implementing-mlops-enterprise-review.md

@@ -0,0 +1,36 @@
+---
+title: 'Book Review: Implementing MLOps in the Enterprise - The Importance of Operations Pipeline Design'
+pubDate: 2026-02-16
+author: 'Nakahara Daisuke'
+tags: ["Book", "MLOps"]
+---
+
+This is a brief book review of "Implementing MLOps in the Enterprise: A Production-First Approach" (Japanese edition) by Yaron Haviv and Noah Gift, published by O'Reilly Japan.
+
+I read this book because I was working on building a cloud-based infrastructure for regularly generating predictions from machine learning models, and I wanted to learn about MLOps.
+Through my current project, I realized that in addition to building highly accurate models, constructing an infrastructure that balances long-term stability with cost reduction was a significant challenge.
+
+MLOps refers to a systematic practical approach that encompasses the entire process of designing, building, and operating the efficient deployment of ML models into production environments.
+MLOps consists of four main components:
+  - Data collection and preparation
+  - Model development and training
+  - ML service deployment
+  - Continuous feedback and monitoring
+
+Similar to what I had felt through my project, MLOps is also defined as having the goal not of building models, but of creating automated ML pipelines that can accept inputs, produce high-quality models, and deploy them into application pipelines.
+
+The most impactful learning for me was "start with designing continuous operations pipelines first, rather than model building."
+This resonated because in my current project, I had adopted the incorrect sequence of advancing model building first, then starting pipeline construction on the cloud after accuracy validation was complete.
+By starting with operations pipeline design first, proper abstraction can be achieved, making it easier to reduce dependency on individuals and accelerate growth.
+
+One of the most interesting chapters in the book is "Chapter 10: Implementing MLOps with Rust."
+The authors' thinking is reflected in this chapter: "If Rust improves operational performance, why not use it?"
+The authors argue that Rust is the most performant and energy-efficient language, and thanks to AI coding tools, it has become much easier to implement than C or C++.
+Reading this chapter, I began to want to learn Rust.
+At the same time, I also became interested in whether it would be possible to implement MLOps with Fortran, the first language I learned and which is widely used for numerical computation.
+
+This book is highly recommended for engineers involved in machine learning projects.
+
+---
+
+> **Note**: The review and translation were assisted by an AI generative model. The author is responsible for the final content.

src/content.config.ts

@@ -2,7 +2,7 @@ import { glob } from "astro/loaders";
 import { z, defineCollection } from "astro:content";
 
 const blog = defineCollection({
-    loader: glob({ pattern: '**/post-*.md', base: "./src/blog" }),
+    loader: glob({ pattern: "**/[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-*.md", base: "./src/blog" }),
     schema: z.object({
         title: z.string(),
         pubDate: z.date(),