MLOps Development Services

Take ML From Notebook to Production with Azumo's MLOps Engineering

Azumo builds and runs the infrastructure that keeps machine learning and LLM models reliable in production. Our MLOps engineers build CI/CD pipelines, monitoring, and automated retraining across AWS SageMaker, Azure ML, Google Vertex AI, and Kubernetes, so the models your team built in the lab actually ship and hold up at scale.

Introduction

How Azumo's MLOps Development Services Work

Azumo provides custom MLOps development services that take AI and LLM models from notebook experiments to production-grade systems. We build and manage the infrastructure for model training, versioning, deployment, monitoring, observability, and retraining, supporting teams on AWS SageMaker, Azure ML, Google Vertex AI, Databricks, and custom Kubernetes clusters.

Most AI projects fail not in model development but in deployment and maintenance. We build CI/CD pipelines for ML models, automated testing that catches performance regressions before release, monitoring dashboards that track accuracy and data drift in real time, and alerting that triggers retraining when performance degrades.

Our MLOps and LLMOps stack includes MLflow for experiment tracking and model registry, Weights & Biases for evaluation, Airflow for orchestration, Feast and Tecton for feature stores, and Docker and Kubernetes for deployment. We design for reproducibility, so every deployment traces back to its exact training data, hyperparameters, and code version.

Production ML Challenges Azumo Helps Solve

Your data science team built an impressive model, then reality hit. Moving from notebook to production exposes infrastructure gaps, monitoring blind spots, and governance risk. Azumo's MLOps engineers build the pipelines and observability that keep models working after launch.

The Problem Azumo's Solution
Manual pipelines don't scale
Without standardized workflows, models take months to deploy and every update needs manual work that introduces errors.		 We automate the ML lifecycle
Our MLOps team builds CI/CD pipelines for training, testing, and deployment so updates ship in days, repeatably, without manual handoffs.
Model drift degrades performance
Production models lose accuracy as data shifts, and without monitoring the decline goes unnoticed until customers complain.		 Azumo monitors drift and retrains automatically
Our MLOps engineers track accuracy and data drift in real time and trigger retraining when thresholds are crossed, so models stay accurate.
Infrastructure costs explode
Teams waste compute on redundant GPU clusters, orphaned databases, and half-built ML stacks.		 Azumo optimizes and automates infrastructure
Our platform engineers implement auto-scaling, spot instances, and resource management with Terraform and Kubernetes to cut compute cost while holding performance.
Governance gaps create risk
Without centralized oversight, teams lack traceability, creating audit failures and compliance violations.		 We build governance in
Azumo implements model registries, lineage tracking, and audit trails so every prediction traces back to its exact model, data, and code version.
Comparison vs Alternatives

DevOps vs. MLOps vs. Full AI/ML Platform Engineering: Which Approach Is Right for Your Business?

Criteria Traditional DevOps MLOps Azumo's Full AI/ML Platform Engineering
What it deploys Application code and configuration. ML models, data pipelines, and serving infrastructure. We build end-to-end AI systems spanning multiple models and services.
Versioning Code versioning with Git. Code, training data, model weights, hyperparameters, and feature definitions. Our MLOps team versions all MLOps artifacts plus prompts, evaluation datasets, and pipeline configurations.
Testing Unit, integration, and end-to-end tests. All standard tests plus model validation, data quality checks, and A/B experiments. Our MLOps experts add adversarial testing, bias audits, and cost-per-inference monitoring.
CI/CD trigger Code commit triggers build and deploy. Code commit, data schema change, or model performance dropping below threshold. Our MLOps team also triggers on scheduled retraining, external model updates, or data distribution drift.
Monitoring Uptime, latency, error rates, resource utilization. All DevOps metrics plus model accuracy, prediction distribution, and data drift scores. We add business KPIs tied to model outputs, SLA compliance, and cost per prediction.
Best for Web applications, APIs, microservices, standard backend services. Teams running 1-10 production ML models that need reliable deployment and monitoring. Azumo's MLOps team supports organizations with 10+ models, multiple ML teams, regulatory audit requirements, or real-time serving at scale.

Key Features of the MLOps Systems We Build

ML Pipeline Development. We build end-to-end training and serving pipelines with MLflow, Kubeflow, and Airflow that automate data ingestion, training, and deployment.

Model Monitoring and Observability. Our MLOps developers implement drift, accuracy, and performance monitoring with Prometheus, Grafana, and OpenTelemetry so issues surface before they hit operations.

Infrastructure Automation. Our MLOps engineers build scalable, cost-managed infrastructure with Terraform and Kubernetes, including auto-scaling and spot strategies.

Feature Stores, Registries, and Governance. Our platform engineers implement feature stores, model registries, lineage, and audit trails across classical ML and LLMOps workflows.

Our capabilities
Our Capabilities for MLOps Development Services

Operationalize ML models with custom MLOps development that speeds up training cycles 4x and reduces infrastructure costs by as much as 75%.

How We Help You:

ML Pipeline Development

We build end-to-end ML and LLM pipelines with Kubeflow, Airflow, and cloud-native tools, automating data ingestion, feature engineering, training, and deployment to reduce your time to production.

Model Monitoring

Our MLOps developers implement observability and monitoring for model performance, data drift, and prediction quality using Prometheus, Grafana, OpenTelemetry, and custom alerting, so issues are caught before they impact operations.

Infrastructure Automation

Our MLOps engineers build scalable ML infrastructure with Terraform, Kubernetes, and cloud services, implementing auto-scaling, resource optimization, and cost management that reduces compute expenses by up to 40% while maintaining performance.

Feature Store Implementation

Our platform engineers develop centralized feature repositories with Feast, Tecton, or custom solutions, keeping training and serving consistent, accelerating model development, and enabling feature reuse across your data science teams.

CI/CD for Machine Learning

We create specialized CI/CD pipelines for ML, including automated testing, model validation, and progressive deployment, with A/B testing, canary releases, and rollback for safe model updates.

Model Registry and Governance

Our MLOps developers establish model registry, versioning, lineage tracking, and experiment management with MLflow, Weights & Biases, or cloud-native solutions, ensuring audit compliance, explainability, and reproducibility across classical ML and LLMOps.

Engineering Services

Our Engineering Services for MLOps Development Services

Our MLOps development and LLMOps consulting enhance the reliability and efficiency of machine learning systems through automated workflows, continuous monitoring, and scalable infrastructure, so you deploy models faster and maintain them with confidence across classical ML and large language model workloads.

Assess and Architect

Our MLOps engineers evaluate your current ML workflow maturity and design a production-ready MLOps architecture, analyzing your data pipelines, model requirements, and infrastructure constraints to create a roadmap that aligns with your business goals and technical stack.

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Build and Automate

Our platform engineers implement end-to-end ML and LLM pipelines using tools like Kubeflow, MLflow, Airflow, and BentoML, creating automated workflows for data processing, feature engineering, model training, evaluation, and validation that reduce deployment time from months to days.

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Deploy and Monitor

We establish production deployment strategies including blue-green deployments, canary releases, and A/B testing, with comprehensive monitoring for model performance, data drift, and system health using Prometheus, Grafana, and custom alerting systems.

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Scale and Optimize

Our MLOps developers continuously improve your MLOps and LLMOps operations through automated retraining pipelines, resource optimization, and horizontal scaling, so your infrastructure efficiently handles growing data volumes, model complexity, and inference workloads while minimizing compute costs.

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Case Study

AI Infrastructure in Production for Our Customers

Deployment, scaling, and operations behind production AI.

Valkyrie

AI Infrastructure Development: Zero-Setup Enterprise AI Access

Read the Case Study
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Stovell AI

Real-time predictive AI trading platform

Read the Case Study

Sparks & Honey

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Benefits
What You'll Get When You Hire Us for MLOps Development Services

Our MLOps practice builds the infrastructure that keeps models reliable after deployment: CI/CD for model updates, automated testing that catches regressions, dashboards for real-time accuracy and drift, and alerting that triggers retraining. We work across AWS SageMaker, Azure ML, Google Vertex AI, and custom Kubernetes clusters.

Faster Time to Production

Our MLOps engineers build automated pipelines and CI/CD that cut deployment time from months to weeks, so your models move from experimentation to production faster.

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Reduced Operational Costs

Our platform engineers implement efficient resource management, auto-scaling, and spot-instance strategies, reducing compute costs by up to 40% while maintaining performance.

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Reliable Model Performance

We build monitoring that detects data drift, performance degradation, and anomalies before they impact your operations, so model quality stays consistent.

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Scalable ML Infrastructure

Our MLOps developers build infrastructure that grows with you, from a single model deployment to hundreds of models across multiple environments.

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Compliance & Governance

Our MLOps engineers implement model lineage, versioning, and audit trails, with governance frameworks that ensure explainability and reproducibility for regulators.

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Seamless Team Integration

Our platform engineers bridge data science and engineering with workflows and tooling that enable collaboration while keeping clear separation of concerns.

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Why Choose Us
Why Choose Azumo as Your MLOps Development Company
Partner with a proven MLOps development company trusted by Fortune 100 companies and innovative startups alike. Since 2016, we've been building intelligent AI solutions that think, plan, and execute autonomously. Deliver measurable results with Azumo.

2016

Building AI Solutions

300+

Successful Deployments

SOC 2

Certified & Compliant

"Behind every huge business win is a technology win. So it is worth pointing out the team we've been using to achieve low-latency and real-time GenAI on our 24/7 platform. It all came together with a fantastic set of developers from Azumo."

Saif Ahmed
Saif Ahmed
SVP Technology
Omnicom

Frequently Asked Questions

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.