Production ML Infrastructure for Enterprise AI Teams
Getting a model to 90% accuracy in a notebook is the easy part. Getting it to production — reliably, at scale, with monitoring, retraining pipelines, and cost controls — requires MLOps. Wolk Inc builds the ML infrastructure layer that turns experimental models into production AI systems.
Training → Serving
Full ML Lifecycle Coverage
RAG + LLMOps
Generative AI Infrastructure
Drift Detection
Production Model Monitoring
48 hrs
Architecture Plan Turnaround
What Wolk Inc Builds for ML Teams
ML Training Pipeline Infrastructure
Reproducible training pipelines on Kubeflow, Metaflow, or Vertex AI Pipelines: data preprocessing, feature engineering, distributed training on GPU clusters, hyperparameter optimisation, and experiment tracking with MLflow or Weights & Biases. Pipeline-as-code for version-controlled, reproducible training runs.
Model Serving & Inference Infrastructure
Low-latency model serving with BentoML, Triton Inference Server, or KServe. REST and gRPC endpoints, model versioning, A/B testing infrastructure, canary deployments for model updates, and auto-scaling for variable inference load. GPU instance optimisation for cost-efficient serving.
Model Monitoring & Drift Detection
Production model monitoring: data drift detection (Evidently, Alibi Detect), prediction quality tracking, feature distribution dashboards, automatic retraining triggers when drift thresholds are breached, and model performance SLO alerting integrated with your existing incident management tooling.
LLMOps & Generative AI Infrastructure
Production infrastructure for LLM-powered applications: RAG pipeline architecture with vector databases (Pinecone, Weaviate, pgvector), LLM gateway design (cost routing, fallback chains, caching), prompt versioning, evaluation frameworks, and cost monitoring dashboards for OpenAI/Anthropic/Bedrock API spend.
Tools & Platforms We Work With
Orchestration
Kubeflow · Metaflow · Airflow · Prefect · Vertex AI Pipelines
Experiment Tracking
MLflow · Weights & Biases · Comet ML · Neptune
Feature Stores
Feast · Tecton · Hopsworks · Vertex AI Feature Store
Model Serving
BentoML · KServe · Triton · TorchServe · SageMaker
LLM Infrastructure
LangChain · LlamaIndex · vLLM · LiteLLM · Bedrock
Monitoring
Evidently · Alibi Detect · Prometheus · Grafana · Arize
ML Engineers Who Build for Production, Not Demos
MLOps Consulting Questions
What is MLOps and why does it matter for production AI?▾
MLOps (Machine Learning Operations) is the set of engineering practices that make machine learning models reliable, reproducible, and maintainable in production. Without MLOps, models degrade silently as data distributions shift, training runs are not reproducible, deployment is manual and error-prone, and there is no systematic way to detect when a model is producing incorrect predictions. MLOps applies software engineering discipline — version control, CI/CD, monitoring, and automated testing — to the ML lifecycle.
How is MLOps different from standard DevOps?▾
MLOps extends DevOps with ML-specific concerns: data versioning (not just code), experiment tracking (training runs have hyperparameters, metrics, and artifacts that standard CI systems do not capture), model registries (versioned, staged model artifacts), feature stores (shared feature computation for training and serving), and model monitoring (statistical drift detection, not just uptime). A DevOps engineer can build the infrastructure layer; the ML-specific tooling and workflows require MLOps-specific expertise.
Which cloud ML platforms does Wolk Inc work with?▾
Wolk Inc implements MLOps on AWS SageMaker, Google Vertex AI, and Azure Machine Learning for clients who want fully managed ML platform services, as well as self-hosted Kubeflow or Metaflow on Kubernetes for clients with data sovereignty requirements or who need more control over infrastructure costs. The right platform depends on your team's existing cloud footprint, model complexity, and data residency requirements.
Does Wolk Inc build RAG pipelines and LLM infrastructure?▾
Yes. Wolk Inc designs and implements RAG (Retrieval-Augmented Generation) pipelines for enterprise LLM applications: document ingestion pipelines, embedding generation at scale, vector database selection and configuration, retrieval chain design, and evaluation frameworks for retrieval quality. We also design LLM gateway architectures that route requests across multiple providers (OpenAI, Anthropic, Bedrock) for cost optimisation and reliability.
How does Wolk Inc handle model monitoring in production?▾
Wolk Inc implements model monitoring at two levels: data quality monitoring (input feature distribution shifts that precede model degradation) and prediction quality monitoring (output distribution shifts and label drift where ground truth is available). Monitoring is implemented using Evidently or Alibi Detect, with dashboards in Grafana and alerting integrated with your on-call tooling. Automatic retraining triggers can be configured to respond to detected drift above defined thresholds.