From Siloed AI to the Enterprise AI Factory
This interactive report explores the strategic shift from isolated projects to a centralized, multi-tenant MLOps platform on Kubernetes, designed to maximize the value of scarce GPU resources and accelerate innovation.
The Core Challenge: GPU Inefficiency
Standard Kubernetes allocates entire GPUs to single tasks, leading to profound underutilization and wasted capital on one of the most expensive assets in modern tech.
~85%
Potential GPU Idle Time
For bursty development and inference workloads in non-shared environments, most of a GPU's lifecycle is spent waiting, representing a massive bottleneck and financial drain.
The Solution: Intelligent GPU Sharing
The cornerstone of the AI Factory is implementing a GPU sharing strategy. The choice between hardware partitioning (MIG) and software multiplexing (Time-Slicing) is the most critical technical decision, dictating performance, isolation, and cost-effectiveness.
MIG: Hardware-Level Isolation
Carves a physical GPU into up to seven smaller, fully independent hardware partitions. It's the gold standard for secure, multi-tenant production environments.
✔ Pros
- Predictable performance & guaranteed QoS
- True hardware fault & memory isolation
- Ideal for production inference SLAs
✖ Cons
- Requires specific, newer hardware (Ampere+)
- Less flexible, fixed partition sizes
Architectural Blueprint
The AI Factory is a layered system of interoperable components, built on Kubernetes and designed to serve the entire MLOps lifecycle.
Data & Feature Layer
Feast Feature Store
MLOps Core Services
Experiment Tracking
Serving Layer
KServe / Triton
Monitoring
Prometheus, Grafana
👩💻 Data Scientist
🛠️ ML Engineer
📱 App Developer
The Developer Workflow
The platform provides a streamlined, automated path from idea to production, abstracting away infrastructure complexity.
1. Define Features
Commit feature logic to the central Feast Git repository, making it discoverable, reusable, and version-controlled.
2. Create Pipeline
Author a training workflow in pure Python using the elegant and intuitive Flyte SDK, abstracting away Kubernetes complexity.
3. Train on GPU
Declaratively request a GPU time-slice with a single line of code. The platform handles scheduling automatically.
4. Deploy Model
The pipeline automatically versions the trained model in a registry and deploys it to a scalable inference service.
The Payoff: Operational Excellence
A well-architected platform provides deep visibility into resource usage and costs, enabling data-driven governance and FinOps.
Real-Time GPU Utilization
Track GPU usage across nodes to identify bottlenecks and ensure efficiency.
Cost Attribution Per Team
Assign costs back to the teams consuming resources, fostering accountability.