Accelerating Model Operations

Integral AI is building the first foundation world model. Our technology, Common Sense AI, extends the generative-AI paradigm into the real-world, enabling and scaling general-purpose real-world intelligence. With applications in robotics, AI assistants, and automated science, we aim to be an infrastructure layer used by developers and enterprises to build upon real-world AI.

San Francisco Bay Area

Location

Integral AI

Software Development

Industry

Challenge

The company faced significant challenges in processing their large volumes of data and experienced slow model creation times that were both resource-intensive and inefficient. They required a solution to accelerate their model delivery while maintaining high accuracy and reducing resource consumption.

Solution

Our team designed and implemented an end-to-end machine learning (ML) pipeline covering data ingestion, training, inference, and deployment. We automated their training processes by developing Directed Acyclic Graphs (DAGs) using open-source tools like Apache Airflow. Additionally, we enabled their developers to work with a dynamic remote Integrated Development Environment (IDE) that was directly connected to their servers.

We built a Kubernetes-based cluster within their GPU ecosystem and developed orchestration layers on top of this infrastructure. This setup allowed for seamless automation and management of their ML workflows. By leveraging built-in distributed training best practices and enabling parallel job execution, our solution achieved a 40x speed improvement while reducing GPU usage by over 67%. Moreover, our solution was environment-agnostic, allowing them to deploy their ML pipeline in different environments, leading to significant infrastructure cost savings.

Discover Our Approach

Automation of ML Workflows

We automated end-to-end ML processes using DAGs built with Apache Airflow, enabling seamless and efficient job scheduling.

STAGE 1

STAGE 2

Kubernetes-Based GPU Cluster

We implemented a robust Kubernetes infrastructure for managing and scaling their GPU workloads, optimizing resource allocation.

Distributed Training Best Practices

By integrating industry-standard distributed training techniques, we enabled parallel execution of models, vastly improving processing speeds.

STAGE 3

STAGE 4

Dynamic Remote IDE Integration

Developers could now work on a dynamic remote IDE linked to the servers, simplifying collaboration and code management.

Environment-Agnostic Deployment

The pipeline was designed to be environment-agnostic, allowing flexible deployment options that saved on infrastructure costs.

STAGE 5

Interested in automation?