Production-grade machine learning pipelines
We build the infrastructure that takes ML models from notebooks to production. From GPU cluster management to model serving at scale, our engineers bridge the gap between data science experiments and reliable, observable ML systems.
MLOps is where software engineering discipline meets data science — and without that discipline, ML projects die in notebooks. The gap between a promising Jupyter experiment and a production model serving real traffic is enormous: reproducible training pipelines, model versioning with lineage tracking, feature stores that guarantee point-in-time correctness, automated evaluation gates that prevent model regressions, canary deployment strategies that limit blast radius, and monitoring systems that detect data drift before your model silently degrades. CloudForge brings DevOps rigor to every stage of the ML lifecycle, treating models as software artifacts that deserve the same CI/CD, testing, and observability as any production service.
We build ML platforms on Kubeflow for pipeline orchestration, MLflow for experiment tracking and model registry, Seldon Core and Triton Inference Server for scalable model serving, and cloud-native ML services (SageMaker, Vertex AI, Azure ML) when managed infrastructure is the right trade-off. Our engineers understand both the infrastructure and the math — we have deployed recommendation models at 10,000+ queries per second with sub-20ms P99 latency, trained LLMs on multi-node GPU clusters with distributed PyTorch, and built feature stores that serve 50M+ features per day with sub-5ms online lookup latency.
The hard part of ML is not training models — it is operating them in production. Models degrade silently as input distributions shift. Training-serving skew introduces subtle bugs that unit tests cannot catch. GPU costs spiral when utilization is not actively managed. Retraining takes hours but stakeholders expect real-time adaptation. CloudForge solves these operational challenges by building ML platforms with automated retraining triggers, shadow deployment for safe model validation, A/B testing infrastructure for controlled rollouts, and comprehensive monitoring that tracks both model performance metrics and infrastructure health in a unified observability stack.
Reproducible training pipelines with versioned data, code, and model artifacts.
Low-latency inference with auto-scaling, batching, and multi-model endpoints.
NVIDIA GPU Operator with fractional sharing, priority queues, and utilization monitoring.
Centralized feature computation with point-in-time correctness and lineage tracking.
Hyperparameter logging, model comparison, and automated experiment-to-production promotion.
vLLM-powered serving with LoRA adapters, quantization, and continuous batching for LLMs.
Dual-layer feature infrastructure with an offline store (data lake or warehouse) for batch feature computation and training dataset generation, and an online store (Redis, DynamoDB) for low-latency feature serving during inference. Point-in-time correctness prevents data leakage in training by ensuring features are joined at the exact timestamp of each training example.
Any ML system where features are shared across multiple models, training-serving skew is a risk, or feature computation is expensive and should be computed once and reused rather than duplicated across model pipelines.
DAG-based training pipeline with stages for data validation (Great Expectations), feature engineering, distributed training (PyTorch DDP or Horovod), hyperparameter tuning (Katib), model evaluation against baseline, and conditional deployment to the model registry. Each stage runs in an isolated container with pinned dependencies and versioned data inputs.
Teams training models regularly (weekly or more frequently) that need reproducible, auditable training runs with automated quality gates and the ability to trace any production model back to its exact training data, code, and hyperparameters.
Multi-model serving platform with Seldon Core orchestrating traffic routing, canary rollouts, and A/B testing across model versions. Triton Inference Server handles the compute — dynamic batching aggregates individual requests into GPU-efficient batches, model ensembles chain preprocessing and inference in a single request, and concurrent model execution maximizes GPU utilization across multiple models.
Production inference workloads requiring low latency, high throughput, multi-model management, or phased rollout strategies. Especially valuable when serving 5+ models that share GPU infrastructure.
Unified platform running JupyterHub for experimentation, Kubeflow for training, Seldon for serving, and Prometheus/Grafana for monitoring — all on shared Kubernetes infrastructure. GPU scheduling with NVIDIA GPU Operator, priority classes for training vs. inference workloads, and namespace isolation per ML team with resource quotas.
Organizations with 5+ data scientists that need shared GPU resources, consistent tooling, and centralized operational oversight without each team building their own ad-hoc ML stack.
dbt models transform raw data into ML features in the data warehouse. Orchestrator (Airflow or Dagster) triggers feature computation, exports to the feature store, kicks off training pipelines when feature freshness thresholds are met, and promotes trained models through the registry. The entire chain — from raw data ingestion to model deployment — is versioned and auditable.
Data teams already using dbt for analytics that want to extend their transformation layer to ML feature engineering, creating a single source of truth for both business intelligence and model training inputs.
Triton Inference Server supports TensorRT, ONNX, PyTorch, TensorFlow, and Python backends in a single server instance. Dynamic batching aggregates requests arriving within a configurable time window into GPU-efficient batches — for transformer models, this often doubles throughput with minimal latency increase. Model ensembles chain preprocessing, inference, and postprocessing in server-side DAGs without network round-trips. Concurrent model execution runs multiple models on the same GPU using CUDA streams, and model warm-up ensures no cold-start latency on first request.
Use dynamic batching — it often doubles throughput for free. Set max_batch_size to match your GPU memory capacity and preferred_batch_size to your typical concurrent request volume. Profile with Triton's perf_analyzer before deploying to find the optimal batch size and instance count for your latency SLA.
Feast manages the feature lifecycle: feature definitions are declared in Python, batch features are materialized from the offline store (BigQuery, Redshift, S3) to the online store (Redis, DynamoDB) on a configurable schedule. Point-in-time joins prevent data leakage in training by reconstructing the feature state at each training example's timestamp rather than using the latest values. Feature freshness SLAs alert when online store values are stale beyond the configured threshold.
Compute features in batch pipelines and serve from Redis or DynamoDB for online inference — never compute features in the serving path. Feature computation in real-time adds latency, introduces failure modes, and makes training-serving parity impossible to verify. Reserve real-time features for session-level signals only.
Kubeflow Pipelines compile Python-defined DAGs into Argo Workflows running on Kubernetes. Each pipeline step executes in an isolated container with explicit input/output artifact declarations. Distributed training with PyTorch DDP uses Kubeflow's PyTorchJob CRD to manage multi-node training with automatic pod placement, NCCL ring-allreduce, and fault tolerance via elastic training. Katib automates hyperparameter tuning with Bayesian optimization, early stopping, and multi-trial parallelism.
Checkpoint every epoch to S3/GCS and use spot instances for training with on-demand fallback. Spot savings of 60-70% more than compensate for occasional preemption. Configure elastic training so jobs can shrink and recover rather than failing entirely when a spot node is reclaimed.
Evidently AI computes drift metrics (PSI, KL divergence, Wasserstein distance) on both input feature distributions and model output distributions. Drift detection runs as a Kubernetes CronJob processing inference logs from the serving pipeline. When drift exceeds configurable thresholds, automated retraining pipelines trigger. Model performance tracking compares real-time metrics (accuracy, AUC, RMSE) against baseline values established during evaluation, with statistical significance testing before alerting.
Monitor input distributions AND output distributions — a model can degrade significantly without measurable data drift if the relationship between features and target changes (concept drift). Set up both statistical drift detectors on inputs and business metric monitors on outputs. Alert on business metrics first; use drift detection to explain why.
NVIDIA Multi-Instance GPU (MIG) partitions A100 and H100 GPUs into isolated instances for inference workloads, allowing 2-7 models to share a single GPU with hardware-level memory and compute isolation. The GPU Operator automates driver installation, device plugin deployment, and GPU feature discovery. Kubernetes scheduler uses extended resources (nvidia.com/gpu) with priority classes to preempt batch training jobs for latency-sensitive inference workloads. Cluster autoscaler provisions GPU nodes based on pending pod resource requests.
Use MIG for inference workloads where models do not need a full GPU — it often reduces GPU cost by 3-5x per model. Reserve full GPUs for training where memory bandwidth and compute are the bottleneck. Configure preemption so inference pods can evict batch training pods during demand spikes, with training automatically resuming from checkpoint.
Training DAG defined in Python using Kubeflow Pipelines SDK. Pipeline stages: data validation checks input schema and distribution against baseline, feature engineering transforms raw data into training features, distributed training runs PyTorch DDP across multiple GPU nodes, model evaluation compares against the production baseline, and conditional deployment promotes to the model registry only if evaluation metrics exceed thresholds. Each component specifies resource requests (CPU, memory, GPU) and artifact I/O.
# Pipeline stages: # ┌──────────────┐ ┌─────────────┐ ┌───────────────┐ # │ Validate │──>│ Transform │──>│ Train (DDP) │ # │ Data │ │ Features │ │ Multi-GPU │ # └──────────────┘ └─────────────┘ └───────┬───────┘ # │ # ┌──────────────┐ ┌─────────────┐ ┌───────▼───────┐ # │ Deploy │<──│ Register │<──│ Evaluate │ # │ (if pass) │ │ Model │ │ vs Baseline │ # └──────────────┘ └─────────────┘ └───────────────┘
Canary rollout configuration for a new model version with shadow traffic and drift detection. The SeldonDeployment resource defines two predictors: the stable model receiving 90% of traffic and the canary model receiving 10%. A drift detector sidecar runs Evidently on incoming requests and model outputs, logging metrics to Prometheus. Traffic split adjusts automatically based on A/B test results, with automatic rollback if the canary model's error rate exceeds 2x the baseline.
# SeldonDeployment resources: # ├── Predictor: stable (90% traffic) # │ └── Container: model-v1, Triton backend # ├── Predictor: canary (10% traffic) # │ └── Container: model-v2, Triton backend # ├── Drift Detector sidecar # │ └── Evidently monitoring on input/output # └── Traffic rules: # └── Auto-rollback if canary error > 2x baseline
Automated model promotion pipeline using MLflow's model registry. After training completes, the pipeline logs the model artifact, evaluation metrics, and training parameters to MLflow. A promotion function compares the candidate model against the current production model on a held-out evaluation set. If the candidate exceeds the production model on all configured metrics (accuracy, latency, memory footprint), it transitions from "Staging" to "Production" automatically. Webhook notification alerts the ML team with a comparison report.
# Promotion flow: # 1. Log model + metrics to MLflow tracking server # 2. Register model version in registry (Staging) # 3. Load production model + candidate model # 4. Evaluate both on held-out test set # 5. Compare: accuracy, P99 latency, memory footprint # 6. If candidate wins all metrics → promote to Production # 7. Archive previous production version # 8. Webhook → Slack notification with comparison table
Feature store-driven recommendations with sub-100ms latency and online model updates.
Production LLM serving with continuous batching, PagedAttention, and multi-LoRA support.
End-to-end pipeline from data labeling to model serving with GPU-accelerated inference.
Traffic-splitting inference endpoints with statistical significance tracking and automatic rollback.
Fraud detection model deployments took 2 weeks of manual work — data scientists handed notebooks to ops engineers who manually converted, tested, and deployed models. No model monitoring, no rollback capability, no experiment tracking. Three production incidents in 6 months from untested model updates.
Built automated ML CI/CD with Kubeflow Pipelines for training orchestration, MLflow for experiment tracking and model registry, and Seldon Core for production serving at 15K QPS. Evidently drift monitoring on transaction feature distributions with automated retraining triggers. Canary deployments with automatic rollback based on fraud detection precision metrics.
“Before CloudForge, deploying a model was a two-week ordeal that nobody volunteered for. Now our data scientists push a model to the registry and it flows through evaluation, canary deployment, and monitoring automatically. We went from dreading deployments to shipping model updates weekly.”
— Head of ML, European Fintech Company
Our ML infrastructure practice sits at the intersection of DevOps engineering and machine learning — a combination that is rare and valuable because most DevOps engineers do not understand model lifecycle management, and most ML engineers do not understand production infrastructure. Our team has deployed recommendation engines serving 10,000+ QPS with sub-20ms P99 latency, built distributed training pipelines on multi-node GPU clusters with PyTorch DDP, and operated feature stores serving 50 million+ feature lookups per day. We speak both languages fluently.
We contribute to Kubeflow and have deep operational experience with the entire CNCF ML ecosystem: Triton Inference Server for multi-framework model serving, Seldon Core for traffic management and A/B testing, MLflow for experiment tracking and model registry, Feast for feature stores, and NVIDIA GPU Operator for GPU lifecycle management on Kubernetes. When Triton's dynamic batching produces unexpected latency spikes, when Kubeflow pipeline steps fail silently due to artifact serialization issues, or when Feast materialization jobs overwhelm your Redis cluster — we have debugged these exact problems in production.
Our engagement model starts with your existing ML workflow — notebooks, scripts, ad-hoc deployments — and progressively introduces infrastructure that makes each pain point disappear. We do not arrive with a pre-built platform and force your team to adopt it wholesale. Instead, we start with the highest-leverage problem (usually model serving or experiment tracking), deliver a working solution in the first sprint, and expand the platform incrementally based on what your data scientists actually need, not what a vendor roadmap prescribes.
Chip Huyen's O'Reilly book covering the full ML systems lifecycle — data engineering, feature engineering, model development, deployment, monitoring, and continual learning. The most practical production ML reference available.
The largest MLOps community with Slack channels, meetups, and conference talks. Real practitioners discussing real problems — feature store selection, model monitoring strategies, GPU cost optimization, and platform architecture decisions.
Official documentation for Kubeflow Pipelines, Training Operators (PyTorchJob, TFJob), Katib hyperparameter tuning, and KServe model serving. The primary reference for Kubernetes-native ML platform engineering.
Goku Mohandas's practical MLOps course covering the entire lifecycle from data to deployment. Hands-on tutorials with real code, not slides. Covers testing, monitoring, and CI/CD for ML — the parts most courses skip.
Our certified engineers are ready to design, build, and operate MLOps & AI Infrastructure solutions tailored to your technical requirements.
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