Merge pull request #27 from BeamStackProj/beamstack

Beamstack at beamsummit blog

Author: olufunbi

content/en/blog/news/launching-beamstack/architecture-beamstack.png

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+---
+date: 2024-09-11
+title: Launching Beamstack at the Beamsummit conference, Google Campus, Sunnyvale CA.
+linkTitle: Launching Beamstack
+description: >
+  Beamstack is an open-source framework currently under development, aimed at facilitating the 
+  deployment of Machine Learning and Gen AI workflow pipelines with Apache Beam on Kubernetes. 
+author: Olufunbi Babalola
+resources:
+  - src: "**.{png,jpg,svg}"
+    title: "Image #:counter"
+---
+
+## Introduction
+The goal of Beam Summit is to connect a community of professionals around the world who use, contribute, and are learning Apache Beam. 
+This annual conference provides space to share use cases, performance, and resource optimizations, discuss pain points, and 
+talk about the benefits of implementing Apache Beam in organizations. The event aims to bring together the Apache Beam community to 
+discuss the project’s status, its technical advances, and its future.
+
+Contents are focused on sharing:
+- New use cases from companies using Apache Beam.
+- Community-driven talks.
+- Technical deep dives.
+- In-depth workshops.
+
+At the beamsummit conference, Beamstack was introduced and launched as an open-source framework aimed at facilitating the deployment of Machine Learning and GenAI workflow pipelines with Apache Beam on Kubernetes. Beamstack was introduced by MavenCode ML Engineers and Product managers in 2 separeate presentations. 
+
+- The first presentation titled **"Beamstack: An Open source Framework for running Machine Learning Pipelines with Apache Beam"** delivered by **Olufunbi Babalola** and **Mathew Fait** introduced beamstack, the product roadmap, how to use beamstack and a call for participation.
+- The second presentation titled **"A Low Code Structured Approach to Deploying Apache Beam ML Workloads on Kubernetes using Beamstack"** delivered by **Charles Adetiloye** and **Nate Salawe** discussed a low code structured approach to deploying apache beam ML workloads on Kubernetes using Beamstack
+
+## Beamstack: An Open source Framework for running Machine Learning Pipelines with Apache Beam
+
+### Agenda
+- Overview of Beamstack
+- Why do you need Beamstack?
+- Beamstack Architecture
+- How to use Beamstack
+- Product Roadmap
+- Call for Participation
+
+
+### Overview of Beamstack
+Beamstack is an open-source framework currently under development, aimed at facilitating the deployment of Machine Learning 
+and GenAI workflow pipelines with Apache Beam on Kubernetes. Beamstack provides a robust Command Line Interface (CLI) that can potentially reduce 
+pipeline deployment complexity and timelines drastically. It also possesses great monitoring and visualization features.  
+
+### Why do you need Beamstack?
+{{< imgproc why Fit "2542x842" >}}
+{{< /imgproc >}}
+- **Configurable Deployment Environment**: With minimal steps you can setup Beamstack on your local minikube, bare metal or cloud infrastructure
+- **Ease of Deployment with Beam Low-code YAML**: Beamstack adopts a low-code approach towards pipeline deployment which makes the process easier and faster
+- **Composable and Reusable Pipeline Components**: Reusable pipeline components designed for easy composition and customization, enabling efficient workflow creation and deployment.
+- **Collaborative Setup for Development Teams**: Beamstack’s modular architecture facilitates collaborative setup’s for various technical teams within an organization
+
+### Beamstack Architecure
+{{< imgproc beamstack Fit "2362x1046" >}}
+{{< /imgproc >}}
+
+### How to use Beamstack
+{{< imgproc install Fit "2424x980" >}}
+{{< /imgproc >}}
+
+- **Install the binary**: Install the beamstack binary from the releases section on the beamstack Github repository
+- **Configre the target environment**: Beamstack initializes your target kubernetes cluster with the necessary components
+- **Select YAML package to deploy**: Run and deploy your YAML pipeline by running the beamstack deploy command
+- **Monitor your running jobs**: Open the grafana dashboard or runner UI to observe your running jobs
+
+### Beamstack Roadmap
+Feature Implementation
+{{< imgproc roadmap Fit "2998x1018" >}}
+{{< /imgproc >}}
+
+Tasks Breakdown
+{{< imgproc tasks Fit "2308x988" >}}
+{{< /imgproc >}}
+
+
+### Call for Participation
+We would like to encourage you to join our discord channel to engage with fellow beamstack developers, become a contributor and drive adoption of beamstack
+
+<a href="https://discord.gg/fYNnNVaEFK">
+  <button class="btn btn-primary py-2 px-5 mb-3">Click to join:<br><b>Beamstack Discord</b></button>
+</a>
+
+## A Low Code Structured Approach to Deploying Apache Beam ML Workloads on Kubernetes using Beamstack
+
+### Agenda
+- Overview of Beamstack
+- Architecture Overview
+- Key Features of Beamstack
+- Beamstack Use Cases / Demos
+- Future Roadmap
+
+
+### Overview of Beamstack
+Beamstack is an open-source framework currently under development, aimed at facilitating the deployment of Machine Learning 
+and GenAI workflow pipelines with Apache Beam on Kubernetes. Beamstack provides a robust Command Line Interface (CLI) that can potentially reduce 
+pipeline deployment complexity and timelines drastically. It also possesses great monitoring and visualization features.  
+
+{{< imgproc overview Fit "2248x930" >}}
+{{< /imgproc >}}
+
+### Architecture Overview
+{{< imgproc layout Fit "2466x1086" >}}
+{{< /imgproc >}}
+
+### Key Features of Beamstack
+{{< imgproc features Fit "2558x850" >}}
+{{< /imgproc >}}
+
+### Beamstack Use Cases & Demos
+Beamstack can currently used in the following ways
+{{< imgproc usecases Fit "2154x698" >}}
+{{< /imgproc >}}
+
+Example Use Case: Creating Text Embedding + Saving it to Vector Database
+{{< imgproc inference Fit "2390x1106" >}}
+{{< /imgproc >}}
+
+Beamstack Demo
+<iframe src="https://drive.google.com/file/d/1YkgvWHE1xM8zxxCtHq9PMF7C2Ky2ndhE/preview" width="1080" height="480" allow="autoplay"></iframe> 
+
+### Future Roadmap
+{{< imgproc future Fit "2034x1228" >}}
+{{< /imgproc >}}
+