Introduction: Why production AI is harder than research

8 min read4 days ago

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In the research lab, a ML model is born inside a clean, isolated environment. Data is pre-curated, training runs are tracked manually, and success is often measured by accuracy on a well-defined benchmark. In the real world, however, models are subjected to an environment that is neither controlled nor static. Data pipelines break, feature distributions drift, GPUs run out of memory, and workload fluactuate without notice. The discipline of operationalizing AI systems — AIOps for general ML and LLMOps for LLMs emerges from these challenges.

What distinguishes AIOps from classical DevOps is not the infrastructure alone, but the constant degradation of assumptions. Unlike a web service, which behaves consistently once shipped, a model’s relevance decays as its data diverges from the distribution it was trained on. This makes production AI an inherently dynamic system. Understanding how to design for this dynamism requires an appreciation of the full lifecycles; from data ingestion to training, from serving to monitoring, from drift detection to retraining.

We’ll explore the infrastructure patterns, tools and practices that separate prototypes from systems capable of running under real-world pressure. We’ll then extend these discussions into the domain of LLMs where scale, latency and unpredictability introduce new categories of problems.

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AIOps + LLMOps infrastructure pipeline

Data Foundations: Feature Stores, Pipelines, and Schema Evolution

The first production challenge is data. Unlike academic datasets, real-world data is never static. Tables evolve, upstream services add new fields, and log structures shift with each new software release. Without a strategy to manage schema evolution, models will fail silently.

A feature store is the cornerstone of modern AIOps infrastructure. It provides a unified interface for both online inference and offline training. Offline feature stores — often backed by data warehouses such as BigQuery, Snowflake, or Delta Lake — guarantee consistency across time, enabling reproducible snapshots for training. Online feature stores, frequently implemented with Redis, Cassandra, or DynamoDB, provide low-latency lookups to serve features at scale. The separation is crucial: without it, models may be trained on one representation of a feature but served with another, introducing subtle but catastrophic mismatches.

Feature engineering in production requires reproducibility. Instead of ad hoc scripts, transformations are encoded as declarative pipelines using frameworks like Apache Beam, Spark, or Airflow DAGs. Every transformation — from scaling numerical features to computing embeddings — is versioned and tested like application code. For example, if a fraud detection model computes “average transaction per customer in the past 30 days,” that logic must be codified, stored, and replayable for both training and inference. Without this rigor, even the most accurate models cannot be trusted in production.

Training Pipelines and Experiment Reproducibility

Training in production is not about running one experiment but about being able to reproduce it months later. Reproducibility is achieved by capturing not just the code but also the hyperparameters, environment, and data version. Tools like MLflow, DVC, or Weights & Biases provide experiment tracking and lineage. Each model artifact in the registry is annotated with metadata: dataset hash, Git commit, environment configuration, and even random seeds.

Why is this important? Imagine deploying a recommendation model that suddenly performs poorly. If you cannot trace back to the exact conditions that produced the current model, rollback becomes guesswork. With a registry, rollback is deterministic: you can promote the previous artifact back into production with confidence.

In practice, training pipelines are codified into CI/CD workflows. A trigger — such as new labeled data arriving in a data lake — launches a pipeline orchestrated by systems like Kubeflow, Tekton, or Argo Workflows. The pipeline handles data preprocessing, training, evaluation, and artifact registration. At the end, the model is stored in a registry, versioned, and ready for deployment. This automation eliminates the fragility of manual retraining.

Deployment Patterns: Serving Models at Scale

Deployment is where machine learning collides with distributed systems. A model is no longer a local function; it is a service that must meet strict SLAs. Kubernetes has become the de facto orchestration layer, with serving frameworks like KServe, Seldon Core, or BentoML providing inference APIs.

A naive deployment runs one model per pod, but scaling requires more sophistication. High-throughput applications rely on request batching, dynamic scaling, and GPU orchestration. For example, an image classification service may batch 64 requests before passing them to the GPU, maximizing utilization without breaching latency thresholds. Advanced setups use autoscaling policies tuned by both CPU/GPU utilization and request latency metrics.

Hybrid serving is another critical pattern. In natural language applications, one may deploy a large transformer model alongside a distilled variant. Low-value or high-volume requests are routed to the distilled model, while complex queries are routed to the full model. Routing decisions can be made via a gateway, balancing cost and accuracy. This strategy is invisible to end users but crucial for sustaining workloads without overspending on GPUs.

Monitoring and Observability: Beyond Latency and Uptime

Traditional DevOps metrics — CPU usage, request latency, uptime — are insufficient for machine learning systems. A model can respond to requests within SLA while producing completely irrelevant predictions. True observability in AIOps requires monitoring the data itself.

Feature drift occurs when the distribution of input features changes over time. Concept drift occurs when the relationship between features and target outcomes evolves. Both degrade model accuracy without affecting latency. Detecting them requires statistical monitoring. Metrics such as Population Stability Index, KL divergence, or Jensen-Shannon distance are computed continuously, comparing live data streams to historical baselines.

When drift is detected, alerts must trigger downstream workflows. For example, if a fraud model’s input feature distribution deviates by more than 5% KL divergence from the training baseline, the system triggers retraining. This closes the loop between monitoring and pipeline execution. Observability also includes capturing ground truth when available. For classification tasks, delayed labels (such as fraud outcomes confirmed days later) are logged and compared against predictions to compute rolling accuracy. This provides not just drift detection but direct performance tracking.

Retraining and CI/CD for Machine Learning

Retraining in production is not optional; it is survival. Models decay in weeks or even days depending on the domain. The question is not whether to retrain, but how.

Incremental retraining updates the model with the latest data, preserving historical weights. This is common in recommendation engines, where patterns evolve quickly. Full retraining discards old weights and trains from scratch, ensuring no bias lingers. This is often used in regulated industries where reproducibility is paramount.

Automation is essential. Retraining pipelines are integrated into CI/CD systems. When drift is detected or new labels arrive, a pipeline is triggered. The new model is evaluated against the incumbent on a validation set, and promotion policies determine whether it replaces the old model. Canary deployments route a fraction of live traffic to the new model, comparing outputs and performance. Shadow testing routes traffic to the new model without affecting production, allowing for silent evaluation. If the new model underperforms, rollback is automatic.

This approach mirrors software CI/CD but adds the complexity of model evaluation. Success is not binary; it depends on business KPIs. In e-commerce, a new recommendation model may only be promoted if it improves click-through rate by 2% or more on shadow traffic. Thus, model governance is both a technical and business decision.

LLMOps: Extending AIOps to Large Language Models

LLMs introduce new operational challenges. They are massive, unpredictable, and expensive to run. Serving them requires specialized infrastructure.

The first challenge is context management. Unlike static classifiers, LLMs depend on prompt length and structure. A production system must manage tokenization, truncation, and context windows dynamically. Token streaming protocols allow responses to be sent incrementally, improving perceived latency.

Retrieval-augmented generation (RAG) is the dominant pattern for grounding LLMs. A query is embedded, compared against a vector database such as Pinecone, Weaviate, or Qdrant, and the top-K results are retrieved. These results are injected into the prompt, grounding the LLM with domain-specific context. This architecture requires maintaining a dual system: the vector database for retrieval and the LLM for generation. Monitoring extends to both components: drift in the embedding space and hallucination in the generated output.

Guardrails become essential. Toxicity filters, prompt injection detection, and hallucination scoring are integrated into the inference pipeline. Some systems implement reinforcement learning from human feedback (RLHF) loops, where human evaluators score outputs, feeding back into fine-tuning pipelines. Others rely on automated evaluation using adversarial prompts. The goal is to catch failure cases before they propagate to users.

Scaling and Cost Efficiency in AI Infrastructure

Running AI at scale is expensive. GPUs cost thousands of dollars per month, and idle resources drain budgets quickly. Engineers optimize cost through several techniques.

Quantization reduces precision (e.g., from FP32 to INT8), lowering memory footprint and speeding inference without significant accuracy loss. Mixed-precision inference leverages hardware accelerators like NVIDIA Tensor Cores, achieving faster throughput. Distillation produces smaller student models from larger teachers, allowing cheaper serving for most traffic.

Sharding is used for ultra-large models. The model is split across multiple GPUs, with each GPU responsible for part of the computation. Frameworks like DeepSpeed and Megatron-LM enable pipeline and tensor parallelism. Triton Inference Server provides a layer of optimization, batching, and scheduling across heterogeneous hardware.

Autoscaling policies are tailored for AI workloads. Instead of scaling pods on CPU utilization alone, systems scale on GPU memory utilization and request latency. Spot instances are leveraged for non-critical retraining jobs, reducing costs while reserving on-demand GPUs for latency-sensitive inference.

Case Study: Fintech Fraud Detection with LLMOps Extensions

Consider a fintech company deploying a fraud detection system enhanced with large language models. The system ingests transaction data, user metadata, and device fingerprints into an offline feature store. Training pipelines produce gradient boosting models for structured data and LLMs fine-tuned on customer support logs to detect fraudulent narratives.

In production, the fraud detection model runs as a service on Kubernetes, with Redis providing low-latency feature lookups. The LLM operates alongside it, using a vector database to retrieve recent fraud cases. Monitoring detects drift when new fraud tactics emerge, triggering retraining. Canary deployments route 5% of transactions to the new model, comparing fraud detection rates. If the model improves recall without raising false positives beyond threshold, it is promoted automatically.

This system combines AIOps discipline — versioning, monitoring, retraining — with LLMOps extensions — retrieval augmentation, hallucination guardrails. The result is a production system that evolves as fraud tactics change, maintaining business resilience.

Conclusion: The Invisible Engineering Behind AI Success

The glamour of AI often lies in model architectures, but the true challenge is operational. AIOps and LLMOps transform machine learning from academic artifacts into production systems. They introduce rigor into data handling, reproducibility into training, robustness into deployment, observability into monitoring, and adaptability into retraining. For large language models, they add guardrails, retrieval systems, and cost optimizations.

Engineers who master these disciplines are not merely deploying models; they are building living systems that adapt, recover, and thrive under real-world conditions. In doing so, they bridge the gap between research and reality, ensuring that AI delivers sustained value, not just benchmark scores.