CI/CD in the AI Era: A Real-World Journey from Model Training to Production Inference

The landscape of software deployment has fundamentally changed with the rise of AI and machine learning. Traditional CI/CD practices that worked beautifully for web applications and microservices now face new challenges when dealing with large language models, GPU clusters, and inference optimization at scale.

Recently, we sat down with an infrastructure engineer from a leading tech company who shared their fascinating journey of building and maintaining a production AI/ML pipeline. Their story perfectly illustrates the complexity—and beauty—of modern AI operations.

The Challenge: From Research to Production at Scale

Imagine this: your data science team has just finished training a breakthrough AI model. It's a large language model based on LLAMA, fine-tuned on proprietary data, and it's showing incredible results in testing. Now comes the hard part—getting it into production where millions of users depend on millisecond latency.

This isn't just about git push and deploying a container anymore. You're dealing with:

• Multi-gigabyte model checkpoints in various formats (PyTorch .pt, TensorFlow .pb)
• GPU-accelerated infrastructure spanning multiple cloud providers
• Inference optimization that can mean the difference between 10x cost or 10x speed
• Canary deployments that need to monitor GPU-specific metrics
• Rollback capabilities when something goes wrong at 2 AM

The Modern AI CI/CD Pipeline: A Deep Dive

Let's walk through how this team solved these challenges, building a robust CI/CD pipeline that handles everything from model training to production inference.

Stage 1: Model Training & Checkpointing

The team works closely with their ML engineers to receive these trained models. But a trained model is just the beginning. To make it production-ready, it needs to go through several critical transformations.

Stage 2: Model Format Conversion (The ONNX Bridge)

Why ONNX? It's the universal translator for AI models. Whether your model was trained in PyTorch, TensorFlow, or another framework, ONNX provides a standardized format that works everywhere. This means:

• Framework agnostic deployment
• Better portability across cloud providers
• Optimization opportunities at the runtime level

Stage 3: AI Acceleration (The Speed Multiplier)

Here's where the magic happens. The team applies AI accelerators—specifically NVIDIA TensorRT and CUDA—to optimize the model for inference. This isn't just a nice-to-have; it's the difference between:

• Affordable inference: 10-50x speedup means 10-50x fewer GPU hours
• Real-time responses: Millisecond latency vs. seconds
• Scalability: Serving more users with the same hardware

The team then leverages NVIDIA Triton Inference Server (TIS), an open-source solution that provides:

• Model versioning and A/B testing
• Dynamic batching for improved throughput
• Multi-model serving on the same GPU
• Built-in metrics and health checks

Stage 4: Containerization & Orchestration

Once optimized, the model gets packaged into a Docker image. This containerization is crucial for:

• Reproducible deployments
• Version control of the entire inference stack
• Isolated environments for different models

Stage 5: Infrastructure Provisioning

The infrastructure automation is a two-step process:

1. Terraform provisions the clusters: Spinning up GPU nodes, networking, and storage across cloud providers
2. Configuration management: Installing GPU drivers, CUDA libraries, and GPU operators to make the environment ready for workloads

This automation is critical when you're managing GPU clusters that need to scale up for training or inference spikes.

Stage 6: Canary Deployment Strategy

Here's where things get interesting. Unlike traditional blue-green deployments, this team uses a canary deployment strategy:

1. 1% exposure: Route 1% of production traffic to the new model version
2. Metric validation: Monitor key performance indicators for 15-30 minutes
3. Progressive rollout: If metrics look good, gradually increase to 10%, then 50%, then 100%
4. Automatic rollback: If any metric degrades, instantly roll back to the previous version

Stage 7: Monitoring & Observability

The team monitors four critical metrics using Prometheus:

• Latency: The holy grail for inference—every millisecond matters
• Throughput: How many requests per second the system can handle
• GPU utilization: Making sure expensive hardware isn't sitting idle
• Memory usage: Preventing out-of-memory errors that crash inference

Prometheus talks directly to GPU operators in the Kubernetes cluster, scraping metrics in real-time. These metrics drive automated decisions during deployment:

• ✅ All metrics green? Continue canary rollout
• ⚠️ Latency spike detected? Pause at current percentage
• 🚨 Critical threshold breached? Automatic rollback initiated

The Tech Stack: What Powers This Pipeline

Here's the complete technology stack that powers this production AI/ML pipeline:

Key Takeaways: Lessons from the Trenches

The Future of AI CI/CD

As AI models continue to grow in size and complexity, the challenges of CI/CD will only intensify. We're already seeing trends toward:

• Model quantization and pruning as part of the CI pipeline
• Distributed inference across multiple GPUs and even multiple regions
• Cost-aware routing that balances performance with cloud costs
• Automated hyperparameter tuning for inference optimization
• GitOps for models where infrastructure and model configs are declarative

The team we spoke with is already experimenting with some of these concepts, continuously evolving their pipeline to meet new demands.

Conclusion: CI/CD Has Evolved

The story shared here illustrates a fundamental truth: CI/CD for AI is different. It requires deep understanding of both traditional DevOps practices and the unique characteristics of machine learning workloads.

You need to think about:

• Model artifacts, not just code
• GPU utilization, not just CPU
• Inference latency, not just request latency
• Model versions, not just software versions
• Accelerator compatibility, not just OS compatibility

But when done right, the results are transformative. Teams can deploy models confidently, scale inference efficiently, and iterate rapidly—all while maintaining the reliability users expect from production systems.