MLOps & Model Lifecycle Management: From Model to Market, Faster.
Stop letting 90% of your machine learning models die in development. We build the automated, scalable, and governed pipelines that turn AI potential into real business value.
Get Your MLOps BlueprintTrusted by Global Leaders to Operationalize AI at Scale
Your Data Scientists Should Build Models, Not Infrastructure.
The "last mile" of machine learning is the hardest. Your team excels at research and building powerful models, but deploying, governing, and maintaining them in production is a different beast entirely. It's a world of brittle scripts, manual handoffs, compliance headaches, and silent model decay that kills ROI. You're stuck in a reactive loop, wasting your most valuable talent on firefighting instead of innovation. We build the robust, automated MLOps foundation that bridges the gap between your data science lab and real-world business impact, ensuring your AI initiatives deliver continuous, measurable value.
Why Partner with CIS for Enterprise MLOps?
We're not just tool installers. We are strategic partners who build resilient, scalable, and governed ML systems that align with your business objectives.
Accelerated Time-to-Value
Don't spend 12-18 months building a platform from scratch. We leverage pre-built components and best practices to deploy your MLOps foundation in weeks, not years, getting your models into production and generating ROI faster.
Enterprise-Grade Governance
With our CMMI Level 5, SOC 2, and ISO 27001 pedigree, we embed security and compliance into every step. We deliver full data lineage, model versioning, access control, and audit trails for transparent and trustworthy AI.
Vendor-Agnostic Expertise
We are experts across the entire MLOps landscape (AWS, Azure, GCP, Kubeflow, MLflow). We design a best-of-breed solution that fits your existing stack and future needs, avoiding vendor lock-in.
Full-Lifecycle Ownership
Our service doesn't end at deployment. We provide continuous monitoring for model drift and data quality, automated retraining pipelines, and ongoing optimization to ensure your models perform at their peak.
AI-Enabled Efficiency
We practice what we preach. Our own AI-enabled developers and processes bring an extra layer of efficiency and insight to your project, ensuring smarter solutions and faster delivery cycles.
20+ Years of Trust
Since 2003, we've been the reliable engineering backbone for startups and Fortune 500s. We bring decades of enterprise software delivery discipline to the dynamic world of machine learning.
Dedicated MLOps PODs
Get a cross-functional team of ML engineers, DevOps specialists, and data architects. Our POD model ensures you have all the required skills, managed under a single, outcome-focused umbrella.
Proactive Model Monitoring
Stop waiting for models to fail silently. We implement proactive monitoring for data drift, concept drift, and performance degradation, triggering alerts and automated actions before they impact your business.
Reproducibility & Scalability
We build systems where every experiment, dataset, and model is versioned and reproducible. This creates a scalable foundation that supports hundreds of models and thousands of deployments with confidence.
Our End-to-End MLOps Services
We provide a comprehensive suite of services to manage the entire machine learning lifecycle, from initial strategy to long-term production maintenance.
MLOps Strategy and Maturity Assessment
Before building, we blueprint. We assess your current ML processes, tools, and team skills to create a tailored MLOps roadmap. This strategic plan aligns your technical implementation with your business goals, ensuring you invest in the right capabilities at the right time for maximum impact and a clear path to AI maturity.
Key Outcomes
- Clear roadmap aligning AI initiatives with business goals.
- Identification of key process bottlenecks and risks.
- A prioritized plan for technology adoption and skill development.
CI/CD for Machine Learning (ML Pipelines)
We automate the path from code to production. We design and implement robust CI/CD pipelines that automatically build, test, validate, and deploy your models. This eliminates manual errors, ensures consistency, and dramatically increases the speed and reliability of your model releases.
Key Outcomes
- Increase model deployment frequency by over 10x.
- Reduce deployment-related errors and rollbacks.
- Enforce quality gates and automated testing for all models.
Feature Store Implementation
We centralize your most valuable asset: features. By implementing a feature store, we create a single source of truth for feature engineering, serving, and discovery. This prevents duplicate work, ensures consistency between training and serving, and allows data scientists to reuse high-quality features across multiple models.
Key Outcomes
- Eliminate training-serving skew for improved model accuracy.
- Accelerate model development through feature reuse.
- Provide a governed, centralized catalog of ML features.
Model Monitoring & Drift Detection
Models degrade in the real world. We implement comprehensive monitoring solutions to track operational metrics (latency, errors) and, more importantly, performance metrics like data drift, concept drift, and prediction accuracy. Get alerted the moment a model's performance starts to decline, not after it impacts your bottom line.
Key Outcomes
- Proactively detect and address model performance issues.
- Maintain high levels of model accuracy and business value.
- Automate alerts and trigger retraining pipelines based on drift.
ML Governance & Compliance
We build trust into your AI systems. Our governance framework provides complete visibility and control over your ML lifecycle. We implement model registries for versioning, access control to protect sensitive data and models, and generate audit trails to meet regulatory requirements like GDPR and CCPA.
Key Outcomes
- Ensure regulatory compliance and reduce risk.
- Provide a complete, auditable history of every model.
- Manage model versions, stages, and permissions effectively.
ML Platform Engineering
We build the paved road for your data scientists. For organizations seeking to scale, we engineer a complete internal ML platform. This provides a standardized, self-service environment for your teams to develop, train, and deploy models efficiently, abstracting away the underlying infrastructure complexity.
Key Outcomes
- Empower data scientists with self-service ML tools.
- Standardize ML workflows across the entire organization.
- Improve resource utilization and reduce infrastructure costs.
Our Proven MLOps Implementation Process
We follow a structured, agile approach to ensure your MLOps foundation is built right, delivering value at every stage.
1. Discover & Architect
We start with a deep dive into your existing ML workflows, infrastructure, and business goals. We then architect a future-state MLOps solution tailored to your specific needs, complete with a technology stack recommendation and a phased implementation roadmap.
2. Foundational Setup (MVP)
We rapidly build the core of your MLOps platform. This includes setting up Infrastructure as Code (IaC), a source control strategy, and an initial CI/CD pipeline for a pilot model. The goal is to deliver a working, automated workflow quickly.
3. Scale & Enhance
With the foundation in place, we expand capabilities. This phase involves integrating advanced features like a feature store, automated model monitoring, and sophisticated retraining triggers. We onboard more models and teams onto the platform.
4. Govern & Optimize
We focus on enterprise-readiness. We implement robust governance, security controls, and model explainability tools. We also optimize pipelines for cost and performance, ensuring your MLOps platform is efficient and secure at scale.
5. Manage & Evolve
MLOps is not a one-time project. We offer ongoing managed services to operate, maintain, and continuously improve your ML platform. We keep your systems up-to-date with the latest technologies and best practices, ensuring long-term success.
Real-World MLOps Success Stories
See how we've helped leading companies transform their AI capabilities from experimental projects to core business drivers.
The Challenge
The client's data science team was creating highly effective fraud detection models, but the manual deployment process was slow, error-prone, and lacked governance. It took weeks to get a new model into production, leaving them vulnerable to emerging fraud patterns.
- Inconsistent testing and validation processes.
- No version control for models or data.
- Lack of monitoring to detect model performance degradation.
- High operational burden on the data science team.
Our Solution
We designed and implemented a secure, cloud-native MLOps platform on AWS. The solution featured a fully automated CI/CD pipeline using AWS SageMaker, Step Functions, and a centralized model registry.
- Automated pipeline for training, testing, and deploying models.
- Implemented data and model versioning with DVC and Git.
- Set up real-time monitoring for concept drift and data quality.
- Established a governance framework with clear audit trails.
The Challenge
The company's recommendation engine was critical for revenue but was struggling to scale. Different teams were rebuilding the same features, leading to wasted effort and inconsistencies. Training-serving skew was a major problem, causing the online model's performance to be much lower than offline tests.
- Duplicated feature engineering efforts across teams.
- Inconsistent feature calculations leading to model errors.
- No centralized way to manage and serve features at low latency.
- Difficulty in maintaining fresh features for real-time predictions.
Our Solution
We implemented a centralized feature store using Feast and Google Cloud Platform (GCP). This provided a single source of truth for features, accessible for both batch training (BigQuery) and low-latency online serving (Cloud Spanner).
- Created a unified repository for thousands of user and item features.
- Automated feature computation and backfilling pipelines.
- Ensured consistency between offline training and online inference.
- Enabled feature discovery and reuse across the organization.
The Challenge
The client needed to develop and deploy deep learning models for medical image analysis while adhering to strict regulatory requirements (HIPAA, FDA). They required a fully reproducible and auditable ML pipeline to prove the safety and efficacy of their AI models.
- Need for complete traceability from data to prediction.
- Strict versioning requirements for data, code, and models.
- Complex validation protocols for model updates.
- Secure handling of sensitive patient data (PHI).
Our Solution
We developed a HIPAA-compliant MLOps platform on Microsoft Azure, using Azure ML and MLflow for tracking and reproducibility. The entire infrastructure was deployed via Terraform, and all actions were logged for audit purposes.
- End-to-end experiment tracking and model lineage.
- Immutable, versioned datasets stored in a secure environment.
- Automated validation pipeline against a "golden dataset."
- Role-based access control and robust security measures.
Our MLOps Technology Stack & Tools
We are experts in the modern, cloud-native MLOps ecosystem. We select and integrate the best tools to build a solution that's right for you.
Including deep expertise in platforms and frameworks like Kubeflow, MLflow, Airflow, DVC, Feast, Seldon Core, and more.
What Our Clients Say
We build more than just platforms; we build lasting partnerships founded on trust and tangible results.
"CIS delivered a production-grade MLOps pipeline in three months, something that would have taken our internal team over a year. Their expertise in Kubernetes and cloud automation is second to none. It's been a massive accelerator for our AI product roadmap."
"The automated model monitoring system is phenomenal. We now catch data drift weeks earlier than before, preventing silent failures and saving us from making costly business decisions based on stale models. The peace of mind is invaluable."
"We needed a partner who understood both machine learning and enterprise-grade security. CIS's deep knowledge of governance and their SOC 2 compliance gave us the confidence to automate our most critical risk models."
"The CIS team felt like a true extension of our own. They were incredibly responsive, technically brilliant, and always focused on our outcomes. They didn't just build a platform; they upskilled our entire team in MLOps best practices."
"The ability to reliably A/B test models in production has been a game-changer for our product development. Thanks to the MLOps framework from CIS, we can now iterate on AI features much faster and with greater confidence."
"We were drowning in technical debt from ad-hoc ML deployments. CIS helped us standardize our entire process. The new platform is scalable, maintainable, and has significantly reduced our operational costs."
Frequently Asked Questions
Clear answers to common questions about our MLOps services.
While you can, it's a significant undertaking that often distracts from your core mission. Building a production-grade MLOps platform requires a rare mix of skills in DevOps, data engineering, and ML. It typically takes a dedicated team 12-18 months and involves significant trial and error. By partnering with us, you leverage our experience from dozens of deployments to get a best-practice, scalable platform in a fraction of the time, allowing your data scientists to focus on what they do best: building models.
Our approach is flexible and vendor-agnostic. We start by understanding your current environment—whether it's AWS, Azure, GCP, or a hybrid setup. We use open-source tools and industry standards to integrate seamlessly with your existing data lakes, warehouses, and CI/CD systems. Our goal is to augment and automate your stack, not force a complete replacement.
The ROI comes from several areas: 1) **Speed:** Drastically reducing the time it takes to get models into production. 2) **Efficiency:** Freeing up expensive data scientists from manual operational tasks. 3) **Performance:** Proactively monitoring and preventing model degradation, which preserves the business value of your AI. 4) **Risk Reduction:** Ensuring governance and compliance, avoiding costly fines or reputational damage. Clients typically see a positive ROI within the first year through increased deployment velocity and operational savings.
Security is paramount. As a SOC 2 and ISO 27001 certified company, we build security into the foundation of the MLOps platform. This includes implementing role-based access control (RBAC), encrypting data at rest and in transit, managing secrets securely, and ensuring that the entire infrastructure is deployed as code for auditability. We help you maintain a secure and compliant ML environment.
We typically start with a 2-4 week assessment and discovery phase to create a detailed roadmap. From there, we move into an agile implementation, usually starting with an MVP pipeline for a single model to deliver value quickly. We offer flexible engagement models, including fixed-price projects for specific deliverables, time & materials for ongoing development, or a dedicated MLOps POD for continuous management and evolution of your platform.
Ready to Unlock the True Value of Your AI?
Stop wrestling with infrastructure and start deploying machine learning models that drive real business results. Let's build your scalable, automated, and governed MLOps foundation together.
Schedule Your Free MLOps Consultation