AI MLOps: Building the Right Infrastructure

Enterprises are leaning heavily into artificial intelligence and machine learning, but building scalable models that perform well in training and in the real world is not the only challenge they are facing. Many models never make it to production or fail to deliver value once deployed due to poor operational practices, highlighting the importance of machine learning operations (MLOps) and building the right AI infrastructure. As enterprises accelerate AI adoption, the demand for robust AI MLOps strategies is rapidly increasing. 

In this blog, we’ll review machine learning ops, why they matter, and how to build the MLOps infrastructure needed to confidently scale your models in production.

Key highlights:

• Well-designed MLOps infrastructure is essential for AI models to reliably go from development to production with reduced model failures.

• Implementing scalable and secure MLOps workflows makes deployment cycles faster, simplifies collaboration, and optimizes resources.

• Structured governance, monitoring, and automation support regulatory compliance and continuous improvement.

• Mirantis k0rdent AI provides scalable and secure MLOps infrastructure to streamline deployment, monitoring, and lifecycle management across hybrid and multi-cloud environments.

What Is MLOps?

Machine Learning Operations (MLOps) standardizes model development, deployment, and maintenance by combining data science, DevOps, and machine learning. Despite common misconceptions, MLOps is much more than just a set of tools; it is a discipline integral to successful model deployment. Structured ML workflows that support reproducibility, scalability, and efficiency are especially important as organizations scale their AI efforts.

Why a Solid MLOps Framework Is Critical for Your Business

AI MLOps is no longer a choice, but an essential business function. Without a strong framework, enterprises experience problems with model failure, inefficiency, and compliance risk. 

The main challenges include:

• Slower Time-to-Value from AI Projects: Without standardized ML workflows, models take much longer to train, test, and deploy. This significantly delays outputs, leading to lost opportunities and lower ROI.

• Difficulty Scaling Model Deployment: Manual pipelines are more likely to struggle with large datasets or multiple complex models, when compared to automated pipelines

• Governance and Audit Gaps: Traceability and auditing suffer if there is no standardized process. Compliance issues are also more common when there isn’t a big-picture view of all operations. 

• High Infrastructure and Talent Waste: Manual operations lead to inefficient use of compute and human resources, which could be better utilized elsewhere. 

• Increased Risk of Performance Drift: If there is no systematic monitoring built into a workflow, model degradation may not be caught until it’s too late.

The Main Components of an MLOps Pipeline

Creating an effective MLOps pipeline requires a system that supports scalability, reproducibility, and long-term performance. Each piece plays a critical role in creating an efficient and powerful workflow. Let’s take a look at the core components of an MLOps pipeline. 

Data Versioning and Management

Data versioning and management ensure that the datasets used by machine learning models remain reliable. Version control keeps track of dataset changes. Data lineage tracking records where data was collected, which transformations were applied to it, and who modified it at what times. 

Data compliance with industry security standards, such as GDPR or HIPAA, often requires auditable lineage and retention policies.

Model Development and Training

MLOps pipelines emphasize standardized and collaborative model development in order to create higher-quality models in less time. Standardized environments, such as containers or virtual environments, enable models to work on all machines. Automating data preprocessing, feature selection, and augmentation makes the development phase significantly faster. 

Pinpointing issues also becomes much easier if teams diligently track results, hyperparameters, code versions, and data versions. 

Continuous Integration and Delivery

CI/CD pipelines move models smoothly from development to production. The automated testing of models validates accuracy, performance, and edge cases before deployment. CI/CD also combines data pipelines, model code, and environment configurations to support reproducibility. Manual effort is reduced due to deployment automation, which pushes models to production environments automatically.  

Model Monitoring and Retraining

Model monitoring includes real-time tracking of metrics such as accuracy, precision, recall, and latency. Another big part of monitoring is drift identification, which identifies changes in the model’s underlying patterns. Automated alerts and dashboards also notify teams of degradations or anomalies. 

Additionally, models should be retrained periodically or on demand if performance drops.

Governance and Metadata Tracking

Governance ensures compliance, auditability, and full model MLOps lifecycle visibility. Maintaining logs is also necessary to meet many regulatory requirements. Role-Based Access Control (RBAC) restricts sensitive model or data access to authorized users only. 

Centralized dashboards track all active and archived models, while model lineage and metadata track which data, code, and parameters produced each model. 

MLOps vs AIOps: What’s the Difference?

MLOps primarily supports the ML lifecycle, while AIOps automates IT operations using AI. MLOps is mainly used by data scientists and ML engineers to standardize and speed up ML workflows. 

Let’s take a closer look at their core differences:

Benefits of Investing in Machine Learning Ops

There are several benefits of a mature MLOps practice, such as efficiency, scalability, and business impact. The main outcomes include:

Faster Experimentation and Deployment Cycles

By automating workflows and standardizing processes, organizations can speed up experimentation and deliver AI models to production faster. This lets enterprises capitalize on opportunities as they arise by cutting down turnaround time. Here are some ways that MLOps supports this speed:

• Reduced Delays: New models can be updated for testing or deployment without manual intervention, thanks to automated pipelines.

• Parallel Experiments: Isolated environments make it possible for data scientists to run multiple experiments simultaneously.

• Rapid Prototyping: Standardized CI/CD workflows accelerate the promotion of models from development to staging environments. 

• Faster Feedback: Shorter feedback loops let enterprises take full advantage of new data trends and business opportunities.

Higher Model Accuracy in Production

Without a rigorous MLOps pipeline, a model’s accuracy can decline when it moves from development to production. Enterprises rely on model accuracy to align with real-world conditions as data evolves. MLOps helps models achieve higher model accuracy through the following methods:

• Continuous Monitoring: MLOps workflows include monitoring for data or concept drift, as well as any other anomalies that might signal performance degradation.

• Automated Retraining: If performance dips, automatic retraining will use updated data to restore accuracy.

• Model Version Comparison: Compare current and previous models to identify which configurations lead to the best performance.

Lower Infrastructure and Ops Overhead

Managing AI without automation can lead to wasted resources and effort. MLOps reduces this waste and increases efficiency while lowering costs. Infrastructure overhead and operational complexity are significantly reduced with MLOps due to:

• Efficient Compute Utilization: Due to automated scaling, models will only consume computing resources when needed.

• Dynamic Scaling with Cloud: Cloud-native MLOps can automatically allocate GPUs or TPUs during peak periods and scale down otherwise.

• Reduced Manual Maintenance: Deployment, monitoring, and troubleshooting require less manual engineering effort.

Stronger Compliance and Version Control

MLOps increases accountability and trust in model outputs while making it easier for enterprises to meet compliance requirements. Especially in regulated industries, enterprises must be able to trace and explain every model decision. Structured and standardized ML workflows make compliance and version control more effective with:

• End-to-End Traceability: Every model is linked to its code, data, and parameters.

• Automated Documentation: Metadata and lineage tracking automatically generate records, making it easier to meet industry regulations around explainability, audit logs, and reproducibility.

• Version Control: Any result can be reproduced with the exact version of the model and dataset.

Cross-Team Visibility and Collaboration

Successful AI workflows require alignment across data science, DevOps, and machine learning teams in order to make models that can be deployed, monitored, and trusted across the enterprise. MLOps helps teams work together with:

• Unified Workflows: AI MLOps platforms centralize model code, data pipelines, and deployment workflows for all teams.

• Role-Based Access: Securely allow data scientists, DevOps engineers, and machine learning engineers to work together without concerns over sensitive data.

• Clear Communication: Automated CI/CD pipelines reduce miscommunication during model and data transfer.

How the MLOps Workflow Is Enhanced with AI 

MLOps workflows are powerful, but they can be further enhanced with AI. Artificial Intelligence can be used to augment MLOps with smarter automation, better optimization, and more adaptive systems. AI can even identify model drift, manage retraining, and scale orchestration. 

Here are the main features artificial intelligence offers: 

• AI-Powered Model Monitoring: AutomatedAI can identify unusual outputs, outliers, or performance dips that signal underlying issues. AI monitoring can also predict failures before they happen, triggering proactive measures to mitigate damage. 

• Smart Retraining and Drift Detection: If the relationship between inputs and outputs changes, AI can automatically retrain models. AI can even compare model performance with previous thresholds to determine if retraining is needed. 

• Dynamic CI/CD Pipeline Tuning: High-value or high-risk models can automatically be prioritized for testing or continuous deployment. Resource availability and prior failure patterns can also be taken into account to increase efficiency.

• Augmented Data Labeling: AI can auto-label simple cases while humans focus on complex or ambiguous situations; this significantly reduces manual labor for large datasets.

• Adaptive Resource Allocation: Resource usage can be automatically adjusted and will be allocated to critical models first during high-demand times. Tasks can be automatically distributed across clusters or cloud regions for better efficiency.

Building Enterprise AI MLOps Infrastructure: 5 Steps

AI MLOps solutions have become a strategic necessity, but designing and building infrastructure can be challenging without the right implementation plan. From assessing readiness to selecting architecture and scaling tools, there are many important factors to consider. Here is a high-level overview to help you get started:

1. Assess Current ML Maturity

Before adopting any new processes, it is critical to understand your current ML workflows. It is important to identify gaps in infrastructure, team skills, and data pipelines so that upgrades can be targeted effectively. Start evaluating your current state with these steps:

• Evaluate Existing ML Pipelines: Are there existing pipelines for model development, testing, and deployment? How are models versioned and monitored?

• Identify Infrastructure Gaps: Are the current storage, compute, and CI/CD systems capable of handling large-scale ML workloads?

• Team Readiness Assessment: Do your ML, DevOps, and data engineers have any skills gaps?

• Data Quality and Accessibility: How is the quality, completeness, and accessibility of the data being used for model training?

2. Define Governance and Access Controls

Without proper governance, enterprises can run into compliance violations, data misuse, and a lack of accountability. The following steps are critical to avoid issues and maintain MLOps security standards:

• Compliance Requirements: List regulations such as GDPR or HIPAA that your organization must adhere to, and the resulting requirements that models must follow.

• Access Control Policies: Implement Role-Based Access Control (RBAC) to restrict data and model access.

• Data Privacy and Encryption: All data used in model training and AI inference must be encrypted to meet privacy standards.

3. Select Scalable Tools and Platforms

Choosing the right tools and platforms is extremely important, as this decision must facilitate growth, integrate with existing systems, and support long-term flexibility. 

When choosing tools and platforms to use, it’s important to prioritize the following: 

• Scalability and Reliability: Make sure tools and platforms can handle large datasets, multi-node training, and an increasing number of models, users, and data.

• Cloud-Native and Hybrid: Find options that support on-premise and cloud environments. 

• Modular and Extensible: Choose tools that can integrate with current and future workflows while avoiding vendor lock-in.

4. Establish CI/CD and Testing Pipelines

CI/CD and testing pipelines are the heart of any robust MLOps framework, as models must be deployed reliably and quickly to be valuable. To achieve this, CI/CD and testing pipelines should include: 

• Continuous Delivery: Automatically push validated models to production.

• Automate Building and Testing: Models can automatically be unit tested, performance tested, and bias checked before deployment.

• Infrastructure as Code (IaC): Standardize deployment environments to increase reproducibility.

• Rollback Mechanisms: Be prepared to return to previous stable models in case of performance issues.

5. Monitor and Iterate Continuously

Once a model is deployed, it must be monitored and consistently improved in order to maintain performance standards. Continuous iterations are needed to keep models accurate, efficient, and relevant. It’s worthwhile to focus on the following while setting up monitoring:

• Real-Time Performance Monitoring: Track key metrics such as accuracy, precision, recall, latency, and resource utilization.

• Logging and Observability: Troubleshooting relies on detailed logs of predictions, errors, and system health.

• Iterative Improvements: Models need constant evaluation, updates, and optimization to remain reliable.

Selecting the Best MLOps Tools

Choosing effective MLOps tools is integral to building an efficient MLOps workflow. Enterprise tooling should support modular growth, align with existing team workflows, and reduce overhead wherever possible. 

Here are some key points to consider when selecting MLOps tools:

• Open MLOps Architecture and Extensibility: Tools should work well with existing data storage, pipelines, and compute resources. Additionally, avoid vendor lock-in and streamline integration with new technologies by opting for open source AI infrastructure. 

• Support for Experiment Tracking and Metadata: Make sure that the tools you choose can track hyperparameters, dataset versions, and experiment results. This is also useful when comparing different versions of models.

• Scalable Infrastructure Integration: To improve cost efficiency, it helps if tools can scale up and down. Tools should also work with on-prem and cloud environments for maximum flexibility.

• Built-In Monitoring and Rollback Support: MLOps Monitoring is essential for detecting drift, anomalies, and failures before they snowball into bigger issues. If performance issues arise, automatic rollbacks revert to previous models in order to keep things running smoothly.

• Compatibility with Common ML Frameworks: The best MLOps tools support TensorFlow, PyTorch, XGBoost, Scikit-learn, and ONNX formats. This allows teams to switch between frameworks easily. 

MLOps Best Practices 

To achieve sustainable, secure, and reliable AI operations, it’s important to stick to MLOps best practices. These tenets center around reproducibility, testing, security, and governance. 

Here is a breakdown of the four main principles:

1. Automate Model Testing and Validation

Testing is the foundation of reliable model deployment, while validation upholds model performance standards before production. Automating these processes prevents errors and saves time, especially if you focus on the following:

• Unit Tests: Components such as data preprocessing and model inference should be validated individually.

• Bias and Fairness Checks: Ethical AI tests should be carried out to detect biases in predictions.

• CI/CD Integration: Automate these tests into your MLOps pipeline to avoid manual errors.

2. Implement Strong Access and Audit Controls

AI models often handle sensitive data, making security nonnegotiable. Strong access and audit controls keep a record of every action and only allow access to authorized users. Key features of strong access and audit control include:

• Role-Based Access Control (RBAC): Access to sensitive data and models should be restricted to certain authorized users only.

• Data Security: Encrypt data in transit and at rest to protect sensitive information.

• Comprehensive Audits: Keep track of every model modification and deployment.

3. Build for Reproducibility and Traceability

Reproducibility builds trust in AI models, and traceability simplifies compliance. Rigorous reproducibility and traceability practices also make it possible to quickly identify the source of issues, which is crucial for MLOps workflows. To make sure that models can be audited, debugged, and explained with full transparency, pipelines should incorporate: 

• Version Control: Store the code, data, and hyperparameters along with every model version for auditing and rollback.

• Environment Consistency: Use containers or virtual environments so that conditions can be replicated whenever needed.

4. Plan for Continuous Monitoring and Feedback Loops

It is also valuable to include continuous monitoring and feedback loops in your AI workflows. Continuous monitoring helps detect issues faster, while feedback loops allow models to improve with real-world input. For ideal results, focus on:

• Real-Time Metrics: KPIs like accuracy, latency, and resource consumption should be tracked at all times.

• Feedback Loops: Integrate manual or systemic feedback to improve model retraining after deployment. 

Mirantis Supports Scalable, Secure MLOps

Mirantis enables secure, enterprise-grade AI MLOps through cloud-native infrastructure, open-source orchestration, and compliance-first pipelines. Mirantis k0rdent AI streamlines model deployment, monitoring, and lifecycle management across cloud, hybrid, on-prem, and edge environments. Its scalable and modular architecture optimizes resource utilization and eliminates vendor lock-in, making it a strong choice for building enterprise MLOps infrastructure.

Contact us to speak with one of our experts and see how Mirantis empowers enterprises to optimize MLOps infrastructure, streamline model lifecycles, and confidently scale AI.