An AI Platform for Structured Infrastructure Access

AI platform engineering empowers teams by providing seamless access to the infrastructure needed for AI model development. We streamline workflows, enhance scalability, and enable teams to focus on innovation without barriers.

Starting Point

AI teams face challenges in accessing and managing the infrastructure needed for model development. These challenges include synchronizing GPU resources, managing tools and library versions, enabling team collaboration, and debugging remote model training. These obstacles divert focus from innovation and slow down progress, creating inefficiencies in AI development workflows.

Objective

Our AI platform empowers AI teams by providing easy access to required infrastructure, streamlining GPU allocation, simplifying tool and library management, and enabling smooth collaboration. By removing operational barriers, we allow teams to focus on innovation and accelerate the development of AI models.

Added Value​

We accelerate experiment cycle times by up to 60% and experiment repeatability by integrating MLOps, streamlining workflows, and eliminating bottlenecks in infrastructure, tooling, and collaboration.

From Challenges to Solutions

Managing GPU Node Infrastructure is Challenging

Managing GPU node infrastructure involves ensuring efficient utilization to avoid high costs, scheduling workloads to maximize performance, addressing hardware and software compatibility, version management, and scaling resources to meet demand. Monitoring, and securing the infrastructure adds complexity, requiring specialized tools and expertise.

Managing Distributed Training and Cross-Team Scheduling

Distributed training of large models faces challenges like communication overhead, synchronization in parallelism, and ensuring fault tolerance. Efficient resource allocation is crucial to avoid bottlenecks and cross-team workload scheduling demands fair resource sharing, balancing priorities, and accommodating dynamic workloads.

Inconsistent Experiments and Artifact Management

Ensuring experiment repeatability and managing results effectively is challenging without consistent environment setups and automated version control for code, data, and hyperparameters. These shortcomings can lead to reproducibility issues. Additionally, artifact management becomes unreliable without proper logging and structured result storage.

Efficient GPU Operations with AI Platform Engineering

Our AI platform solves GPU infrastructure challenges by automating resource management, optimizing workload scheduling, and scaling on-demand. It maximizes GPU utilization, reduces costs, and dynamically adjusts resources to match workload demands. Real-time monitoring, maintenance, and integrated security ensure performance and reliability, while compatibility management ensures smooth operation across systems.

Efficient AI Workload Management and Fair Access

Our AI platform provides seamless GPU access to AI teams, supporting distributed model training, and managing resource allocation dynamically. It optimizes workloads, ensures synchronization, and enforces fair scheduling policies to balance priorities and minimize contention. Teams can focus on development while the platform ensures efficient and scalable infrastructure management and experiment execution.

Solving Reproducibility with MLOps Integrations

An AI platform combined with MLOps solves these challenges by ensuring consistent environments and automated version control for code, data, and hyperparameters, enabling full experiment reproducibility. With containerization and automated workflows, it eliminates inconsistencies, while structured logging and artifact storage streamline result management. This approach ensures reliable and reproducible experimentation.

Technical Deep Dive

AI Platform Details

Our AI platform is designed to empower organizations by simplifying the complexities of GPU infrastructure management. We built upon Kubernetes and Ray Train to enable efficient distributed training and scalable AI workloads. The platform can be deployed seamlessly on custom GPU clusters or integrated with existing IaaS cloud provider solutions, offering organizations the adaptability they need to meet their unique infrastructure requirements.

With automated installation scripts, we ensure a rapid setup, minimizing the time required to get systems operational. Its robust observability features, powered by Prometheus and Grafana, provide comprehensive monitoring and insights into system performance. These tools enable real-time tracking of resource utilization, model training progress, and system health, facilitating proactive maintenance and optimization of workloads.

The platform abstracts the complexities of GPU resource management by orchestrating resource allocation and workload scheduling across GPU environments. This ensures optimal GPU utilization while balancing throughput and runtime requirements. Sophisticated scheduling policies enhance fairness and efficiency, enabling teams to run scalable, distributed workloads without the need for manual intervention.

By integrating MLOps principles, the platform provides a unified environment for experiment reproducibility and artifact management. Automated version control for code, data, and hyperparameters guarantees the traceability and repeatability of experiments. Containerization ensures consistent and reliable environment setups.

Together, these capabilities form a comprehensive solution that addresses the most pressing GPU infrastructure challenges, empowering teams to focus on innovation and model development rather than operational overhead. The result is an efficient, reliable, and scalable platform that adapts to evolving AI workload demands, ensuring performance, cost-efficiency, and seamless integration across diverse environments.