MLOps Engineering Services

Frequently Asked Questions

• Q:

  What MLOps services does Azumo provide?

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  Azumo builds and operates the infrastructure that keeps AI models reliable in production. Our MLOps services include automated training pipelines, model versioning and registry, A/B testing frameworks, monitoring and alerting for model drift, automated retraining triggers, and CI/CD for machine learning. We build on MLflow, Kubeflow, Weights & Biases, and custom orchestration using Airflow and Prefect. Infrastructure runs on AWS SageMaker, Azure Machine Learning, Google Vertex AI, or self-hosted Kubernetes clusters depending on your requirements. Azumo has deployed and maintained production ML systems since 2016, with Valkyrie (our AI infrastructure platform) running across AWS, RunPod, and Hetzner. SOC 2 certified with nearshore engineering teams across Latin America working in US time zones.

• Q:

  Why do companies need MLOps?

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  Without MLOps, AI models degrade silently. Training data drifts from production reality, model accuracy drops, and no one notices until business metrics suffer. MLOps solves this by automating the cycle of training, evaluation, deployment, monitoring, and retraining. Companies need MLOps when they move past a single proof-of-concept to multiple production models that require consistent performance guarantees. Key triggers include: models that need retraining on fresh data weekly or monthly, teams managing more than two or three production models simultaneously, compliance requirements that demand reproducibility and audit trails, and organizations where a 5% accuracy drop translates directly to revenue loss. MLOps transforms ML from a one-time project into a sustainable, measurable capability.

• Q:

  What is the difference between MLOps and DevOps?

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  DevOps automates software delivery: code goes from repository to production through CI/CD pipelines. MLOps extends this to machine learning, which introduces three additional challenges that standard DevOps cannot handle. First, ML has data dependencies, not just code dependencies: a model is a function of its training data, and data changes independently of code. Second, ML requires experiment tracking: teams test dozens of hyperparameter combinations and need to reproduce any prior result. Third, ML models degrade in production without code changes because input data distributions shift over time. MLOps adds data versioning (DVC, LakeFS), experiment tracking (MLflow, Weights & Biases), model registries, automated retraining, and production monitoring to the standard DevOps toolkit.

• Q:

  What MLOps tools and platforms does Azumo work with?

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  Azumo works with MLflow for experiment tracking and model registry, Kubeflow for orchestrating training pipelines on Kubernetes, Weights & Biases for experiment visualization and model comparison, and Airflow and Prefect for workflow orchestration. For infrastructure, we deploy on AWS SageMaker, Azure Machine Learning, Google Vertex AI, and self-hosted Kubernetes with GPU node pools. We use DVC and LakeFS for data versioning, Docker and Helm for containerization, and Terraform for infrastructure as code. For model serving, we use TorchServe, Triton Inference Server, and BentoML depending on latency and throughput requirements. Valkyrie, our internal platform, demonstrates our approach: unified model access running on multi-cloud infrastructure across AWS, RunPod, and Hetzner.

• Q:

  How does Azumo monitor ML models in production?

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  We build monitoring systems that track four categories: model performance metrics (accuracy, precision, recall, F1 on live predictions versus ground truth), data drift (statistical tests comparing production input distributions to training data), system metrics (latency, throughput, error rates, GPU utilization), and business metrics (conversion rates, customer satisfaction, or whatever outcome the model is supposed to improve). Alerting triggers automated retraining when drift exceeds defined thresholds or when accuracy drops below acceptable levels. We implement monitoring dashboards using Grafana, Datadog, or custom solutions built on your existing observability stack. For LLM-based systems, we additionally monitor hallucination rates, token costs, and prompt injection attempts using tools like LangSmith.

• Q:

  How long does it take to implement an MLOps pipeline?

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  An initial MLOps pipeline for a single model with automated training, versioning, and basic monitoring can be implemented in 3-6 weeks. A comprehensive MLOps platform supporting multiple models with automated retraining, A/B testing, canary deployments, and full observability typically takes 2-4 months. Timeline depends on the number of models to support, existing infrastructure maturity, data pipeline complexity, and compliance requirements. The fastest path starts with a single high-value model and builds the pipeline around it, then extends to additional models incrementally. Azumo brings pre-built templates for common MLOps patterns, reducing setup time. Our nearshore teams work in US time zones with sprint-based delivery.

• Q:

  What is model drift and how does Azumo handle it?

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  Model drift occurs when the statistical properties of production input data diverge from training data, causing model predictions to become less accurate over time. There are two types: data drift (input distributions change) and concept drift (the relationship between inputs and correct outputs changes). Example: a fraud detection model trained on 2023 transaction patterns becomes less accurate as fraud tactics evolve in 2024. Azumo detects drift using statistical tests (Population Stability Index, Kolmogorov-Smirnov, Jensen-Shannon divergence) applied to incoming data distributions. When drift exceeds defined thresholds, our pipelines trigger automated retraining on recent data, evaluate the retrained model against held-out test sets, and deploy only if the new model outperforms the current production version.

• Q:

  What security and compliance does Azumo implement for MLOps?

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  Azumo is SOC 2 certified and implements role-based access controls for model registries, encrypted model artifacts at rest and in transit, signed model provenance to prevent tampering, and comprehensive audit trails that log every training run, evaluation, and deployment. For regulated industries, we ensure full reproducibility: any production prediction can be traced back to the exact model version, training data snapshot, and hyperparameters used. This is critical for HIPAA, GDPR, and financial compliance where regulators may require explanation of automated decisions. We implement model governance workflows that require human approval before deploying models that affect high-stakes decisions. Infrastructure deploys on private cloud or on-premises when data sovereignty requires it.