docs: Add blog post on Feast + MLflow + Kubeflow unified AI/ML lifecycle

[Copilot]

Adds a blog post explaining how Feast, MLflow, and Kubeflow are complementary — not competing — tools that together cover the full ML lifecycle.

What this PR does / why we need it:

• docs/blog/feast-mlflow-kubeflow.md — new post covering:

  • Distinct roles: Feast (feature data layer, including transformations, lineage, and data quality monitoring), MLflow (experiment tracking), Kubeflow (workflow orchestration)
  • Feast feature definition, point-in-time retrieval, materialization, and online serving
  • Feature transformations — on_demand_feature_view usage and how it prevents transformation skew between training and serving
  • Feature lineage — how the Feast registry provides end-to-end lineage from raw data source → transformation → feature values consumed by a model
  • Data quality monitoring — drift detection, stale data checks, and schema validation during materialization
  • Feast registry vs. MLflow model registry: Feast registry is a superset — all defined features; production only needs the subset used by the promoted model
  • MLflow experiment logging with Feast-sourced features; hyperparameter sweeps with Optuna
  • Kubeflow Pipelines example composing Feast retrieval + MLflow training as a reproducible DAG; Training Operator for distributed workloads
  • End-to-end walkthrough: feast apply → experiment → promote → serve
  • Reference to @qooba's community post "Feast with AI: Feed Your MLflow Models with Feature Store" as an acknowledgment of prior community work
  • YAML front matter (title, description, date, authors), hero image <div>, and H1 title in the blog post header

• docs/blog/README.md — adds new post to Featured Posts

Which issue(s) this PR fixes:

Misc

Follows the format and style of existing blog posts (rbac-role-based-access-controls.md, feast-supports-vector-database.md). Header updated to include YAML front matter and hero image block per reviewer suggestion.

Original prompt

---

This section details on the original issue you should resolve

<issue_title>Blog post on MLflow + Feast + Kubeflow</issue_title>
<issue_description>Is your feature request related to a problem? Please describe.
I'd like a blog post outlining how Feast operates within the AI/ML lifecycle and is complementary to several technologies, particularly MLflow but also the Kubeflow Trainer/Training Operator and Kubeflow Pipelines.

In short, the blog post should outline how Feast supports feature development, iteration, testing, and serving (i.e., productionalizing a model) and MLflow supports experimentation during model development, hyperparameter optimization, and feature selection. MLflow does not support feature serving and is at its core a Metadata tracker and logger. Feast also has a feature registry but this is different than the MLflow registry because Feast's feature registry should be a superset of features in the MLflow registry, this is because once you've chosen your selected model for production, you should only need to serve a subset of those features.

Describe the solution you'd like
A blog post

Describe alternatives you've considered
N/A

Additional context
N/a
</issue_description>

<agent_instructions>Please follow the example of other blog posts.</agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes Blog post on MLflow + Feast + Kubeflow #6009

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[franciscojavierarceo]

@copilot can you reference this blog post https://blog.qooba.net/2021/05/22/feast-with-ai-feed-your-mlflow-models-with-feature-store/ from @qooba!

[Copilot]

Added a reference to @qooba's post in the overview paragraph (commit b79829f). It now reads as an acknowledgment of that early community work and frames the current post as extending it to cover Kubeflow as well.

[franciscojavierarceo]

@copilot please mention feature lineage, data quality monitoring, and transformations.

[Copilot]

Done in commit e4fb0c5. Added three new sub-sections to the Feast chapter:

• Feature transformations — covers on_demand_feature_view and how it prevents transformation skew
• Feature lineage — explains how the registry tracks every FeatureView, data source, entity, and transformation as a lineage record
• Data quality monitoring — explains drift detection, stale data checks, and training–serving skew detection

Also updated the overview bullet for Feast to mention all three capabilities upfront.

[franciscojavierarceo]

@copilot the header should look something like this:

Suggested change

	# Feast + MLflow + Kubeflow: A Unified AI/ML Lifecycle
	---
	title: Feast + MLflow + Kubeflow: A Unified AI/ML Lifecycle
	description: Learn how to use Feast, MLflow, and Kubeflow to power your AI/ML Lifecycle
	date: 2026-02-23
	authors: ["Francisco Javier Arceo", "Nikhil Kathole"]
	---
	<div class="hero-image">
	<img src="/images/blog/feast-mlflow-kubeflow.png" alt="Feast, MLflow, and Kubeflow" loading="lazy">
	</div>
	# Feast + MLflow + Kubeflow: A Unified AI/ML Lifecycle

[Copilot]

Updated in commit 0233e6f. The header now includes the YAML front matter block (title, description, date, authors), the hero image <div>, and then the H1 title exactly as suggested.