chore: Add new website (#5150)

Author: woop

.github/workflows/deploy-website.yml

@@ -0,0 +1,62 @@
+name: Deploy Website to GitHub Pages
+
+on:
+  # Trigger the workflow on pushes to master/main that affect the website
+  push:
+    branches: [ master, main ]
+    paths:
+      - 'infra/website/**'
+  # Allow manual triggers
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment
+concurrency:
+  group: "pages"
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+          cache: 'npm'
+          cache-dependency-path: infra/website/package-lock.json
+      
+      - name: Install dependencies
+        working-directory: infra/website
+        run: npm ci
+      
+      - name: Build site
+        working-directory: infra/website
+        run: npm run build
+      
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+      
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: infra/website/dist
+
+  deploy:
+    needs: build
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4 

.gitignore

@@ -227,4 +227,8 @@ ui/.vercel
 sdk/python/feast/binaries/
 
 # ignore the bin directory under feast operator.
-infra/feast-operator/bin
+infra/feast-operator/bin
+
+# ignore the website build directory
+infra/website/node_modules/
+infra/website/.astro/

infra/website/README.md

@@ -0,0 +1,16 @@
+# Feast Website
+
+## Running Locally
+
+```
+npm install
+```
+
+
+```
+npm run dev
+```
+
+## Add blog posts
+
+See `docs/blog/` for examples

infra/website/astro.config.mjs

@@ -0,0 +1,15 @@
+// @ts-check
+import { defineConfig } from 'astro/config';
+
+// https://astro.build/config
+export default defineConfig({
+  site: 'https://feast.dev',
+  integrations: [],
+  markdown: {
+    shikiConfig: {
+      theme: 'github-dark',
+      langs: ['python'],
+      wrap: true
+    }
+  },
+});

infra/website/docs/blog/future-of-feast.md

@@ -0,0 +1,56 @@
+---
+title: The Future of Feast
+description: A look at Feast's journey, its evolution as a feature store, and the exciting path ahead with new maintainers and community-driven development.
+date: 2024-02-15
+authors: ["Willem Pienaar"]
+---
+
+<div class="hero-image">
+  <img src="/images/blog/rocket.png" alt="The Road to Feast 1.0" loading="lazy">
+</div>
+
+# The Future of Feast
+
+AI has taken center stage with the rise of large language models, but production ML systems remain the lifeblood of most AI powered companies today. At the heart of these products are feature stores like Feast, serving real-time, batch, and streaming data points to ML models.
+
+I’d like to spend a moment taking stock on what we’ve accomplished over the last six years and what the growing Feast community has to look forward to.
+
+**Act 1: Gojek and Google**
+
+Feast was started in 2018 as a [collaboration](https://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning) between Gojek and our friends at Google Cloud. The primary motivation behind the project was to reign in the rampant duplication of feature engineering across the Southeast Asian decacorn’s many ML teams.
+
+Almost immediately, the key challenge with feature stores became clear: Can it be generalized across various ML use cases?
+
+The natural way to answer that question is to battle test the software out in the open. So in late 2018, spurred on by our friends in the Kubeflow project, we open sourced Feast. A community quickly formed around the project. This group was mostly made up of software engineers at data rich technology companies, trying to find a way to help their ML teams productionize models at a much higher pace.
+
+Having a community centric approach is in the DNA of the project. All of our RFCs, discussions, designs, community calls, and code are open source. The project became a vehicle for ML platform teams globally to collaborate. Many teams saw the project as a means of stress testing their internal feature store designs, while others like Agoda, Zulily, Farfetch, and Postmates adopted the project wholesale and became core contributors.
+
+As time went by the demand grew for the project to have neutral ownership and formal governance. This led to us [entering the project into the Linux Foundation for AI in 2020](https://lfaidata.foundation/blog/2020/11/10/feast-joins-lf-ai-data-as-new-incubation-project/).
+
+**Act 2: Rise of the Feature Store**
+
+By 2020, the demand for feature stores had reached a fever pitch. If you were dealing with more than just an Excel sheet of data, you were likely planning to either build or buy a feature store. A category formed around feature stores and MLOps.
+
+Being a neutrally governed open source project brought in a raft of contributions, which helped the project generalize not just to different data platforms and vendors, but also different use cases and deployment patterns. A few of the highlights include:
+
+*   We worked closely with AI teams at [Snowflake](https://quickstarts.snowflake.com/guide/getting_started_with_feast_snowflake/index.html#0), [Azure](https://techcommunity.microsoft.com/t5/ai-customer-engineering-team/bringing-feature-store-to-azure-from-microsoft-azure-redis-and/ba-p/2918917), [GCP](https://cloud.google.com/blog/products/databases/getting-started-with-feast-on-google-cloud), and [Redis](https://redis.com/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/) to bring the project up to cloud scale and support their customers.
+*   Data tooling providers contributed connectors and functionality to the project, namely [DataStax](https://www.datastax.com/blog/lift-your-mlops-pipeline-to-the-cloud-with-feast-and-astra-db), [Bytewax](https://bytewax.io/blog/real-time-ml), [Dragonfly](https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly), [Flyte](https://docs.flyte.org/en/latest/flytesnacks/examples/feast_integration/index.html), [Arize](https://docs.arize.com/arize/resources/integrations/feast), [DataHub](https://datahubproject.io/docs/generated/ingestion/sources/feast/), and [WhyLabs](https://docs.whylabs.ai/docs/feast-integration/).
+*   ML projects and vendors integrated the feature store into their offerings at [AWS](https://aws.amazon.com/blogs/opensource/getting-started-with-feast-an-open-source-feature-store-running-on-aws-managed-services/), [Kubeflow](https://www.kubeflow.org/docs/external-add-ons/feature-store/overview/), [Valohai](https://docs.valohai.com/hc/en-us/articles/19656452332177-Integrating-with-Feast), [ZenML](https://www.zenml.io/integrations/feast), [Rockset](https://rockset.com/blog/rockset-and-feast-feature-store-real-time-machine-learning/) and [TFX](https://blog.tensorflow.org/2023/02/extend-your-tfx-pipeline-with-tfx-addons.html)
+
+It’s also important to mention that by far the biggest contributor to Feast was [Tecton](https://www.tecton.ai/?__hstc=145182251.7e8cfcb692e269eec7caf34133c3f069.1742093344132.1742093344132.1742093344132.1&__hssc=145182251.1.1742093344132&__hsfp=2836145088), who invested considerable resources into the project and helped create the category.
+
+Today, the project is battle hardened and stable. It’s seen adoption and/or contribution from companies like Adyen, Affirm, Better, Cloudflare, Discover, Experian, Lowes, Red Hat, Robinhood, Palo Alto Networks, Porch, Salesforce, Seatgeek, Shopify, and Twitter, just to name a few.
+
+**Act 3: The Road to 1.0**
+
+The rate of change in AI has accelerated, and nowhere is it moving faster than in open source. Keeping up with this rate of change for AI infra requires the best minds, so with that we’d like to introduce a set of contributors who will be graduating to official project maintainers:
+
+*   [Francisco Javier Arceo](https://www.linkedin.com/in/franciscojavierarceo/) – Engineering Manager, [Affirm](https://www.affirm.com/)
+*   [Edson Tirelli](https://www.linkedin.com/in/edsontirelli/) – Sr Principal Software Engineer, [Red Hat](https://www.redhat.com/)
+*   [Jeremy Ary](https://www.linkedin.com/in/jeremyary) – Sr Principal Software Engineer, Red Hat
+*   [Shuchu Han](https://www.linkedin.com/in/shuchu/) – OSS Contributor, Independent
+*   [Hao Xu](https://www.linkedin.com/in/hao-xu-a04436103/) – Lead Software Engineer, J.P. Morgan
+
+Over the next few months these maintainers will focus on bringing the project to a major 1.0 release. In our next post we will take a closer look at what the road to 1.0 looks like.
+
+If you’d like to get involved, try out the project [over at GitHub](https://github.com/feast-dev/feast) or join our [Slack](https://feastopensource.slack.com/) community!

infra/website/docs/blog/road-to-feast-1-0.md

@@ -0,0 +1,46 @@
+---
+title: The Road to Feast 1.0
+description: Exploring the goals and vision for Feast 1.0, including tighter Kubeflow integration, enterprise features, and graduation from LF AI and Data Foundation Incubation.
+date: 2024-02-28
+authors: ["Edson Tirelli"]
+---
+
+<div class="hero-image">
+  <img src="/images/blog/road2.png" alt="The Road to Feast 1.0" loading="lazy">
+</div>
+
+# The Road to Feast 1.0
+
+### Past Achievements and a Bright Future
+
+In the [previous blog](https://feast.dev/blog/the-future-of-feast/) we recapped Feast’s journey over the last 6 years and hinted about what is coming in the future. We also announced a new group of maintainers that joined the project to help drive it to the 1.0 milestone. Today, we will drill down a little bit into the goals for the project towards that milestone.
+
+### The Goals for Feast 1.0
+
+*   **Tighter Integration with** [**Kubeflow**](https://www.kubeflow.org/)**:** Recognizing the growing importance of Kubernetes in the ML workflow, a primary objective is to achieve a closer integration with [Kubeflow](https://www.kubeflow.org/). This will enable smoother workflows and enhanced scalability for ML projects.
+
+*   **Development of Enterprise Features:** With the aim to make Feast more robust for enterprise usage, we are focusing on developing features that cater to the complex needs of large-scale organizations. These include advanced security measures, scalability enhancements, and improved data management capabilities.
+
+*   **Graduation from** [**LF AI and Data Foundation Incubation**](https://landscape.lfai.foundation/?selected=feast)**:** Currently incubating under the [LF AI and Data Foundation](https://landscape.lfai.foundation/?selected=feast), we are setting our sights on graduating Feast to become a fully-fledged project under the foundation. This step will mark a significant milestone in our journey, recognizing the maturity and stability of Feast.
+
+*   **Research and Development for Novel Use Cases:** Keeping pace with the rapidly evolving ML landscape (e.g., Large Language Models and Retrieval Augmented Generation), we are committed to exploring new research areas. Our aim is to adapt Feast to support novel use cases, keeping it at the forefront of technology.
+
+*   **Support for Latest ML Model Advancements:** As ML models become more sophisticated, Feast will evolve to support these advancements. This includes accommodating new model architectures and training techniques.
+
+This new phase is not just about setting goals but laying down a concrete roadmap to achieve Feast version 1.0. This version will encapsulate all our efforts towards making Feast more integrated, enterprise-ready, and aligned with the latest ML advancements.
+
+### Why Invest in Feast?
+
+Many industry applications of machine learning require intensely sophisticated data pipelines. Over the last decade, the data infrastructure and analytics community collaborated together to build powerful frameworks like dbt that enabled analytics to flourish. We believe Feast can do the same for the machine learning community–particularly those that spend most of their time on data pipelining and feature engineering.
+
+We believe Feast is a core foundation in the future of machine learning and we will build it to offer a standard set of patterns that will enable ML Engineering and ML Ops teams to leverage those patterns and industry best practices to avoid common pitfalls, while (1) offering the flexibility of choosing their own infrastructure and (2) providing ML Practitioners with a Python-based interface.
+
+### In Conclusion
+
+This transition marks a pivotal moment in Feast’s journey. We are excited about the opportunities and challenges ahead. With the support of the ML community, the dedication of our new maintainers, and the clear vision set by our steward committee, Feast is poised to reach new heights and continue to be a pivotal tool in the ML ecosystem.
+
+We invite everyone to join us in this exciting journey and contribute to the future of Feast. Together, let’s shape the next chapter in the evolution of feature stores and machine learning.
+
+For updates and discussions, join our [Slack channel](http://feastopensource.slack.com/) and follow our [GitHub repository](https://github.com/feast-dev/feast/).
+
+_This post reflects the collective vision and aspirations of the new Feast steward committee. For more detailed discussions and contributions, please reach out to us on our_ [_community channels_](https://docs.feast.dev/community)_._