Reliable Model Deployment
300+
AI & Data Integrations Delivered
6-10
Weeks to Production-Ready MLOps
40%
Less ML Operations Overhead
Trusted By 600+ Brands
What Is Machine Learning
Operations and Why
Production AI Depends on It
Machine learning operations, or MLOps, is the discipline of applying DevOps principles to the full lifecycle of machine learning models, covering data pipelines, model training, deployment, monitoring, and retraining. Without it, ML models sit in notebooks, deployment is manual and inconsistent, and performance degradation goes undetected until it becomes a visible business problem. With a structured MLOps foundation, organisations can deploy models reliably, scale them across business functions, and maintain accuracy over time as data and conditions change. It is the infrastructure layer that separates an AI experiment from a dependable business capability.
WHAT MLOPS MAKES POSSIBLE
Automated Model Retraining
Real-Time Drift Monitoring
ML Governance & Auditability
MLOps Solutions Across Every Stage of the
Machine Learning Lifecycle
A Trusted MLOps Company Delivering Reliable, Governed, and Scalable Machine Learning Infrastructure
for Enterprise and Growth-Stage Businesses
MLOps Consulting Services
Before building MLOps infrastructure, you need a clear view of where your current process is breaking down and what level of maturity your organisation is ready to adopt. Our MLOps consulting services assess your existing data pipelines, model deployment approach, monitoring gaps, and team capabilities, then produce a phased implementation roadmap. The output is a practical plan your engineering team can execute against, not a theoretical framework that does not account for your actual constraints.
ML Pipeline Design and Automation
Manual ML pipelines break under production pressure. We design and build automated pipelines that handle data ingestion, preprocessing, feature engineering, model training, validation, and registration in a repeatable, version-controlled workflow. Each pipeline is built to your specific data environment and model types, whether you are running computer vision models, NLP systems, or structured data forecasting, and is designed to scale as your model portfolio grows.
ML Model Engineering Services
Getting a model from a research notebook into a production-grade system requires engineering work that most data science teams are not resourced to do alongside model development. Our ML model engineering services handle the translation from experimental code to robust, deployable software, including refactoring, containerisation, API wrapping, schema validation, and integration with your serving infrastructure. The result is models that behave consistently in production rather than only in a controlled notebook environment.
CI/CD for Machine Learning
Continuous integration and deployment for ML involves more complexity than standard software CI/CD because model quality is evaluated differently at each stage. We implement CI/CD pipelines for ML that automate testing of data quality, model performance, and integration behaviour before any version reaches production. This gives teams the confidence to ship model updates frequently without manual sign-off processes that cycles and create deployment backlogs.
Model Monitoring and Drift Detection
A model that performed well at deployment will not necessarily perform well six months later as underlying data distributions shift. We implement monitoring systems that track model predictions, feature distributions, and business-level outcome metrics in real time, alerting when drift or degradation crosses defined thresholds. This is the difference between catching a performance problem on a dashboard and discovering it through a complaint or a financial discrepancy.
Managed Machine Learning Services
Enterprise organisations with multiple models in production across different business functions need more than tooling. They need a managed service layer that handles model operations as an ongoing discipline. Our managed machine learning services for enterprises cover deployment, monitoring, retraining scheduling, performance reporting, and governance across your full model portfolio, giving your data science team the operational support to focus on building new capabilities rather than maintaining existing ones.
Feature Store Implementation
Inconsistent feature engineering is one of the most common causes of training-serving skew, where a model behaves differently in production than it did during training. We design and implement feature stores that centralise feature computation, ensure consistency between training and inference pipelines, and make reusable features available across model teams. This is particularly valuable in enterprise environments where multiple teams are building models that draw on overlapping data sources.
MLOps Platform and Infrastructure Setup
A well-chosen MLOps platform reduces operational complexity significantly, but the wrong choice creates technical debt that compounds over time. We help organisations evaluate, configure, and deploy MLOps platforms suited to their scale, existing cloud infrastructure, and team capabilities. Whether that means implementing Kubeflow, MLflow, SageMaker Pipelines, Vertex AI, or a custom orchestration layer, the setup is designed around your actual workflow rather than a vendor reference architecture.
Why Businesses Choose Commerce Pundit
as Their MLOps Company
Practical Machine Learning Operations Expertise Grounded in Real Production Deployments
Across Multiple Industries and Cloud Environments
End-to-End ML Lifecycle Experience
Our team has experience across the full ML lifecycle, from data engineering and model development through deployment, monitoring, and retraining. We understand what breaks in production, because we have seen it.
Platform-Agnostic Infrastructure Guidance
We configure and build MLOps infrastructure on AWS, Azure, and Google Cloud, recommending the tools and platforms that suit your environment rather than defaulting to a single vendor approach.
Engineering and Data Science Collaboration
MLOps requires both software engineering discipline and an understanding of how ML models behave. Our team bridges both, avoiding the common gap between data science output and production-grade software.
Governance and Compliance Built In
Every MLOps implementation we build includes model versioning, audit logging, access controls, and documentation practices, giving regulated industries and enterprise compliance teams the visibility they require.
Scalable Architecture From the Start
We design MLOps infrastructure to scale with your model portfolio, so the architecture that works for five models in production today does not need to be rebuilt when you are running fifty.
Connection to Broader AI Capability
MLOps does not exist in isolation. Our team connects ML operations to your broader AI development, AI workflow automation, and machine learning development work for a coherent, maintainable AI infrastructure.
Real Results Delivered Across Industries
Retail & eCommerce
MLOps Infrastructure for a Retail Demand Forecasting Platform
Challenge
The data science team had built accurate demand forecasting models but was deploying them manually, with no versioning, no monitoring, and no automated retraining. Model performance was degrading seasonally without the team realising until stockouts and overstock events made the issue visible.
Solution:
We implemented a full MLOps pipeline on AWS SageMaker covering automated retraining triggered by data drift signals, model registry with version control, A/B deployment infrastructure, and a monitoring dashboard tracking forecast accuracy against actuals across all product categories in near real time.
Less manual deployment and maintenance effort
60%
Higher forecast accuracy with automated retraining
18%
Model degradation detection over 12 months
100%
Financial Services
CI/CD and Model Monitoring for a Fintech Risk Scoring System
Challenge
A credit risk scoring model was running in production without monitoring or a structured deployment process. Model updates were infrequent and risky because there was no way to validate performance before full release, and any regression would only be discovered after it had affected lending decisions.
Solution:
We implemented a CI/CD pipeline for the risk model with automated performance validation at each stage, canary deployment to limit exposure during rollout, and a real-time monitoring layer tracking score distribution, feature drift, and downstream decision outcomes against regulatory and business performance thresholds.
Models Under Unified Governance
4X
Fewer Model-Related Incidents
100%
Less Operational Work for Data Teams
35%
Enterprise SaaS
Managed MLOps for an Enterprise SaaS Platform With Multiple Models
Challenge
The platform had 12 ML models in production across recommendation, churn prediction, and anomaly detection use cases, each maintained differently by separate teams with no shared infrastructure, governance, or visibility into overall model health across the portfolio.
Solution:
We designed and implemented a centralised MLOps platform on Google Vertex AI, standardising pipeline architecture, monitoring, and governance across all 12 models. A feature store was introduced to eliminate training-serving skew, and a unified model registry gave engineering leadership visibility into the status and performance of every model in production.
Under Unified MLOps Governance
18,000
in Model-Related Incidents Across the Platform
34%
higher conversion on AI-written pages
19%
Our MLOps Implementation Process
A Structured Approach That Moves You from Fragile Manual ML Workflows to Governed, Automated, Production-Grade Operations
MLOps Maturity Assessment
We begin by evaluating your current ML workflow, from data preparation and training through deployment and monitoring, to establish where the process is manual, inconsistent, or missing entirely. This gives us a clear starting point and shapes the implementation roadmap so early phases address the highest-risk gaps first.
Architecture Design and Platform Selection
Based on the assessment, we design the MLOps architecture that suits your model types, data environment, team capabilities, and cloud infrastructure. Platform selection is made against your actual requirements rather than defaulting to the most popular option, and the design is reviewed with your team before any implementation work begins.
Pipeline and Infrastructure Build
We build and configure the agreed MLOps infrastructure, including data and training pipelines, model registry, CI/CD workflows, serving infrastructure, and monitoring systems. Implementation is phased to deliver functional capability at each stage rather than requiring everything to be in place before any value is realised.
Integration and Team Enablement
MLOps infrastructure only creates value when it is integrated into how your data science and engineering teams actually work. We integrate the new workflow into your existing development process, provide structured guidance to the teams who will use it, and document the operational procedures that keep the system running correctly.
Monitoring and Ongoing Support
Once live, we monitor the MLOps infrastructure itself alongside the models running on it, addressing any issues and optimising pipeline performance as usage grows. Most organisations continue working with us beyond initial implementation to extend capability as their model portfolio expands and operational requirements evolve.
MLOps Platforms, Frameworks, and Tools We Work With
From Pipeline Orchestration and Model Registries to Monitoring Infrastructure and Cloud ML Platforms
MLOps Platforms
Cloud ML Services
Pipeline Orchestration
Model Serving and Deployment
CI/CD and Version Control
Model Monitoring and Observability
Feature Stores
Data and Compute Infrastructure
Frequently Asked Questions
About Our MLOps Services
What are MLOps services?
MLOps services cover the infrastructure, tooling, and processes that enable machine learning models to be deployed, monitored, and maintained reliably in production. This includes automated training pipelines, model registries, CI/CD workflows for ML, monitoring systems, and retraining automation. A specialist MLOps company like Commerce Pundit implements and manages this infrastructure, giving businesses the operational foundation to run machine learning in production with the same reliability standards applied to any other critical software system.
Why do machine learning projects fail without MLOps?
Without MLOps, ML deployment is manual and inconsistent, meaning model updates are infrequent and risky. There is no monitoring to detect when a model starts producing degraded predictions, no automated retraining when data distributions shift, and no governance over which model versions are in production. The cumulative effect is that even high-quality models become unreliable over time, and the data science team spends more time on operational tasks than on building new capabilities.
What is the difference between MLOps and DevOps?
DevOps applies automation, testing, and monitoring principles to software development and deployment. MLOps extends these principles to machine learning, adding complexity because models are not just code. They also depend on data quality, and their performance changes as data distributions shift over time. MLOps therefore includes data pipeline management, model evaluation at each deployment stage, drift monitoring, and retraining automation, which have no direct equivalent in standard software DevOps workflows.
What do MLOps consulting services include?
MLOps consulting services typically begin with a maturity assessment of your current ML workflow, identifying gaps in your deployment process, monitoring coverage, and governance practices. From this, a structured implementation roadmap is produced, prioritising the changes that will reduce the most operational risk or deployment friction first. Consulting can also cover platform selection, team capability planning, and architectural guidance for organisations earlier in their MLOps adoption.
How do you handle model monitoring and drift detection?
We implement monitoring systems that track both model-level metrics, including prediction distribution and confidence scores, and business-level outcome metrics that indicate whether the model is still delivering useful results. Drift detection compares current feature distributions against training baselines and alerts when divergence crosses defined thresholds. Monitoring is configured to your specific model types and business context rather than applying generic monitoring templates that generate noise without actionable signal.
What are managed machine learning services for enterprises?
Managed machine learning services for enterprises provide ongoing operational management of ML models in production, covering deployment, monitoring, retraining scheduling, and performance reporting across a model portfolio. This is suited to organisations with multiple models running across different business functions who need consistent governance and operational discipline without resourcing a dedicated internal MLOps team. We operate as an extension of your data science function, handling the operational layer so your team focuses on model development.
Which cloud platforms do you support for MLOps?
We implement MLOps infrastructure on AWS, Azure, and Google Cloud, using their native ML platforms, including SageMaker, Azure Machine Learning, and Vertex AI, as well as cloud-agnostic tools like MLflow, Kubeflow, and ZenML where appropriate. Platform selection is based on your existing cloud environment, team familiarity, and the specific requirements of your ML workflow rather than a preferred vendor relationship. We also support hybrid and on-premise environments for regulated industries with data residency requirements.
How long does it take to implement MLOps infrastructure?
A focused implementation covering automated pipelines, a model registry, and basic monitoring for a single use case typically takes six to ten weeks. A broader programme covering multiple model types, CI/CD, feature store implementation, and enterprise governance takes longer and is scoped in phases to deliver working capability at each stage. We provide a detailed timeline following the initial maturity assessment, once we have a clear view of your starting point and the scope of what needs to be built.
Can MLOps be applied to generative AI and LLM workflows?
Yes. The core MLOps principles of versioning, automated evaluation, deployment governance, and performance monitoring apply directly to generative AI and LLM workflows. Fine-tuning pipelines, RAG system maintenance, prompt version management, and output quality monitoring all benefit from the same operational discipline as traditional ML. As organisations move generative AI from experiment to production, MLOps infrastructure is what prevents model performance from degrading silently as data, prompts, and underlying models evolve.
How do we get started with Commerce Pundit's MLOps services?
The process starts with a consultation where we discuss your current ML workflow, the models you have in production or approaching production, and the operational challenges you are experiencing. From there, we recommend a starting point, whether a maturity assessment, a focused pipeline build, or a managed service arrangement, with a realistic scope and timeline. There is no obligation at this stage, and the initial conversation is typically where the most useful clarity about priorities and approach emerges.