Outerbounds is now on Google Cloud Marketplace

You can now get started with Outerbounds through Google Cloud Marketplace.

The AI landscape is evolving rapidly, but organizations still face many challenges when trying to build serious ML/AI systems, especially around data privacy and sovereignty . As one of the most privacy-focused organizations in the tech industry, Mozilla needed a solution that could scale machine learning while ensuring that all data and models remained securely within their own environment. This was non-negotiable for them.

"Outerbounds’ ability to keep all our data and models securely within our GCP environment, without ever leaving our control, was critical for us. This ensured we maintained full privacy while scaling our machine learning operations efficiently." — Chelsea Troy, Engineering Leader at Mozilla

Our collaboration with Google Cloud addresses these pain points head-on. Integrating Outerbounds directly within Google Cloud enables teams to develop, scale and deploy ML/AI systems while leveraging Google Cloud’s highly available, high-performance infrastructure.

Outerbounds + Google Cloud: A Full-Stack ML Platform

With Outerbounds on Google Cloud, teams can:

1. Seamlessly integrate with existing Google Cloud services like Cloud SQL, BigQuery, and Google Cloud Storage to store and manage ML artifacts, models, and data.
2. Access scalable Kubernetes-based orchestration to run their ML workloads across thousands of nodes with built-in support for GPU resources, optimizing for both cost and performance.
3. Automate the entire ML workflow, from development to production, with full version control, tracking, and monitoring.
4. Deploy models faster, moving from prototype to production up to 5x faster than traditional methods.
5. Ensure enterprise-grade reproducibility by automatically tracking metadata, logs, and execution results.

“Bringing Outerbounds ML Platform to Google Cloud Marketplace will help customers quickly deploy, manage, and grow their ML platforms on Google Cloud's trusted, global infrastructure." said Dai Vu, Managing Director, Marketplace & ISV GTM Programs at Google Cloud.

Working with Vertex AI

With Outerbounds, you can seamlessly integrate Vertex AI to streamline the entire ML/AI  lifecycle, from data ingestion to deployment. Our integration with Vertex AI allows teams to leverage Google’s advanced ML tools effectively:

• Data Ingestion: With Outerbounds, you can ingest data directly from BigQuery. This enables efficient access to vast datasets, reducing data preparation time and helping you get started with model development faster.
• Model Development on Outerbounds: Once the data is ingested, you can use Outerbounds to develop, train, and fine-tune machine learning models. Our platform offers a comprehensive development environment with support for scalable orchestration, experiment tracking, and version control, allowing teams to iterate on models efficiently and collaboratively.
• Deployment to Vertex AI: After developing a model, deploying it to production is streamlined by integrating with Vertex AI. This allows you to leverage Google’s powerful serving infrastructure, including support for A/B testing, model monitoring, and managed endpoints to ensure high availability and reliability of your deployed models.

By using Outerbounds alongside Vertex AI, you can harness the power of foundation models for a wide variety of applications, including natural language processing, image generation, and more.

Working with Google Kubernetes Engine (GKE)

Outerbounds natively deploys using Kubernetes through Google Kubernetes Engine (GKE), an industry-leading container orchestration platform that acts as a de-risking factor for technical teams. By leveraging GKE, Outerbounds provides a scalable and resilient deployment environment that can grow alongside your ML operations. GKE’s infrastructure ensures that your ML workloads are highly available, allowing for the seamless orchestration of compute resources and making sure that all components work together effectively.

With first-class support for GKE deployments, Outerbounds offers the following key advantages:

• Scalable Infrastructure: Outerbounds integrates natively with GKE to ensure that your infrastructure can adapt in real-time, enabling smooth and efficient scaling as the demand for compute resources fluctuates.
• Simplified Deployment: Outerbounds manages the complexities of Kubernetes deployments so that data scientists and ML engineers can focus on developing and iterating on models without worrying about underlying infrastructure management.
• Cost Efficiency: Using GKE in combination with Outerbounds optimizes resource allocation, allowing organizations to minimize cloud expenses while still ensuring that all ML processes are executed efficiently. 
• Reliability and High Availability: Outerbounds leverages GKE's capabilities for creating redundant, highly available clusters, ensuring that ML models and workflows are always operational. 
• Integrated Monitoring and Logging: Outerbounds’ integration with GKE includes monitoring and logging capabilities to track the performance of ML models and infrastructure. 

With GKE, ML teams can be confident in their ability to scale, manage, and optimize machine learning operations without taking on additional risks or operational burdens.

Faster, More Scalable ML/AI Development

At its core, this partnership enables customers to focus on what matters most—developing impactful ML/AI models—without getting bogged down by the complexities of infrastructure management. With our fully managed ML platform, organizations can:

• Increase the speed of model deployment: Companies can move from a few deployments a year to hundreds, significantly improving model velocity.
• Reduce infrastructure costs: By leveraging Google Cloud’s scalable infrastructure and Outerbounds' optimized orchestration, organizations can minimize cloud expenses while ensuring that compute resources are used efficiently.
• Enhance reproducibility and version control: Outerbounds ensures that every model, experiment, and artifact is automatically tracked, offering full reproducibility at scale. This guarantees that teams can revisit and re-deploy models with confidence.

To learn more about how to get started with Outerbounds on Google Cloud Marketplace, reach out!