--- title: "MLOps and model operations" description: "Training pipelines, model registries, GPU scheduling, inference serving and monitoring, so models are deployed, measured and replaced on purpose." url: https://www.expandware.com/services/ai/mlops-and-model-operations/ section: "Services" topics: ["AI", "Kubernetes", "Slurm", "MLflow", "Kubeflow"] publisher: "Expandware Private Limited" --- # MLOps and model operations Training pipelines, model registries, GPU scheduling, inference serving and monitoring, so models are deployed, measured and replaced on purpose. Models run like production services. A model in a notebook is an experiment; a model in production is a service with uptime, latency, cost and quality to manage. MLOps is the engineering that makes the difference: reproducible training, versioned models and data, serving infrastructure sized to the traffic, GPU capacity shared fairly between teams, and monitoring that notices when a model's answers drift before your customers do. Where it usually starts: - Models are trained on one person's machine and nobody can reproduce last quarter's version. - GPUs are idle half the time and oversubscribed the other half. - Inference costs are rising faster than usage. - Nobody is alerted when a model's accuracy degrades. What is included: - Training pipelines: Reproducible training and fine-tuning with tracked data, code and parameters. - Model registry: Versioned models with lineage, approvals and promotion between environments. - GPU scheduling: Kubernetes or Slurm scheduling that shares accelerators between teams by priority and quota. - Inference serving: Low latency serving with batching, autoscaling and quantization where it holds quality. - Monitoring: Latency, throughput, cost and quality metrics, with drift detection. - Cost control: Utilization reporting and right-sizing for training and inference capacity. Where it sits: MLOps runs the loop that keeps models useful: feedback and outcomes flow back into fine-tuning, new versions pass through the model registry, and evaluation decides what reaches production. This service builds and operates that loop. Decisions to settle early: - Kubernetes or Slurm. Kubernetes suits mixed services and inference; Slurm suits batch training on shared clusters. Some estates run both with a clear boundary. - Cloud, owned or both. Owned accelerators are cheaper at steady use; cloud suits bursts and experiments. The platform is designed so work can move between them. - How much to automate retraining. Automatic retraining keeps models fresh but can ship a bad model quickly. Promotion gates decide what still needs a person. How the work runs: Assess, Current workflow, capacity and costs. Platform, Pipelines, registry and scheduling. Serve, Inference sized to real traffic. Operate, Monitoring, drift detection and retraining. How it is bought: - A fixed-fee assessment: We review how models are trained, deployed and monitored today, and measure accelerator use and inference cost. You receive the gaps, a platform design and a fixed price for building it. - A fixed-price build: Training and deployment pipelines, the registry and promotion workflow, accelerator scheduling, serving and monitoring for the models in scope, at a fixed price. - Running it afterwards: The platform can be handed to your engineers with runbooks, or operated by us monthly, covering capacity, upgrades, failed pipelines and drift alerts. Worked example, models nobody could reproduce. Say a retailer's data science team trains demand forecasting models on workstations and a shared GPU server. Nobody can rebuild last quarter's model, and the GPU server is either idle or fought over. - Assess: We review how models are trained, deployed and monitored today, and measure how the GPU capacity is actually used across the week. - Platform: Training moves to pipelines that record the data, code and parameters of every run, and models are registered with their metrics and approvals. - Schedule: The GPUs are pooled under a scheduler with priorities and quotas for each team, so urgent retraining is not queued behind an experiment. - Monitor: Production models report accuracy against actual sales, and an alert fires when forecasts drift beyond the tolerance the team set. At the end, any model in production can be rebuilt from its record, GPU time is shared by rule rather than by negotiation, and drift is caught by monitoring rather than by the buyers. (An illustration, not a client account.) What you have at the end: - MLOps platform design and infrastructure code: The architecture of the training and serving platform, and the code that builds it, in your repository. - Training and deployment pipelines: Automated pipelines that rebuild a model from its data, code and parameters and promote it between environments. - Model registry with a promotion workflow: Every model version with its lineage, metrics and approvals, and a defined path from experiment to production. - Serving endpoints with load test results: Inference endpoints configured for your traffic, with latency and throughput measured under load. - Monitoring dashboards and alert routing: Latency, cost, quality and drift metrics for each model, with alerts routed to the team that owns it. Standards and platforms worked with: Kubernetes, Slurm, MLflow, Kubeflow, Ray, vLLM, NVIDIA Triton Inference Server, NVIDIA GPU Operator, Prometheus. Where it goes wrong, and what we design out: - Models nobody can rebuild. A model trained on one laptop cannot be audited or rolled back. Every run records its data, code and parameters automatically. - Accelerators used by negotiation. Shared GPUs without a scheduler sit idle or oversubscribed. Quotas and priorities decide who runs, not whoever asks loudest. - Drift noticed by customers. A model degrades quietly as the world changes. Production quality is measured against outcomes, and drift raises an alert to the model's owner. What it changes: - Reproducible models. Any model in production can be traced to the data, code and parameters that produced it. - Accelerators kept busy. Scheduling and quotas raise the utilization of expensive GPUs without starving any team. - Quality you can see. Drift and degradation are alerted on, so retraining is a planned response rather than a crisis. How you will know it is working: - Any production model can be rebuilt from its recorded run. - Accelerator time is shared by agreed priority, with use reported. - A degrading model is flagged by monitoring before users complain. - Inference cost is tracked per model and per request type. Who you work with: - ML platform engineer. Builds the pipelines, registry, scheduling and serving, all as code. - MLOps engineer. Wires monitoring, drift detection and promotion workflows into the platform. - Infrastructure engineer. Sizes and operates the accelerator capacity, on premises or in the cloud. What we need from you: - An inventory of the models in use and who owns each. - Access to the training data, code and current infrastructure. - Data scientists willing to move one model through the new platform first. - Agreed quality measures for the models in production. Common questions: Q: Should we use Kubernetes or Slurm for GPU workloads? A: Slurm suits large, long running training jobs that need whole nodes and gang scheduling; Kubernetes suits mixed workloads and inference. Many clusters run both, and we design the split from your mix of jobs. Q: Can you reduce our inference costs? A: Usually. Batching, caching, quantization, smaller models for simpler requests and autoscaling to real traffic each cut cost, and we measure quality at each step so the savings do not come out of the answers. Q: Do you support on-premises and cloud GPUs together? A: Yes. A common pattern is steady workloads on owned capacity and bursts in the cloud, with one scheduler and one registry across both. Q: How do you detect model drift? A: By monitoring the inputs a model receives and the quality of its outputs against a baseline, with alerts when either moves beyond an agreed threshold and a retraining pipeline ready to run. Q: What is a model registry? A: A system of record for trained models: each version with the data and code that produced it, its evaluation results, who approved it and where it is deployed. It is what makes a production model reproducible and a rollback possible. Q: Can MLOps work with the tools our data scientists already use? A: Generally, yes. Notebooks remain the place for exploration, and the platform takes over when work becomes repeatable. MLflow, Kubeflow and Ray integrate with the common frameworks, so for data scientists the change is mostly in how runs are tracked and promoted, not in how models are written. Q: How do you serve large language models efficiently? A: With serving engines built for them, such as vLLM or Triton, which batch requests and manage GPU memory well, and with quantization where tests show it holds quality. Capacity is sized from measured traffic, and autoscaling keeps idle accelerators to a minimum. Q: Who is on call when a model in production fails? A: The model's owner is accountable for its quality, and platform engineers for the infrastructure it runs on. We define both roles and their alerts during the engagement, and where we operate the platform for you, our engineers take the infrastructure side of on-call. Often paired with: AI data centers and GPU clusters, Kubernetes and platform engineering, Generative AI and agents. --- Canonical page: https://www.expandware.com/services/ai/mlops-and-model-operations/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.