Why MLOps matters: bridging the gap between ML and Operations
- Edwin Kuss
- 7 min
Table of contents
- The driving factors behind the rise of MLOps
- The overlapping issues between MLOps and DevOps
- Shared challenges between DevOps and MLOps — and how DevOps practices solve them
- The unique challenges in MLOps compared to DevOps
- Machine Learning challenges that require MLOps
- Detailed breakdown of MLOps-specific challenges
- The integral parts of a modern MLOps structure
As machine learning (ML) continues to power modern products — from personalized recommendations to predictive analytics — many organizations face a new challenge: how to move ML models from the research stage into reliable, scalable production. This is where MLOps (Machine Learning Operations) comes in.
MLOps applies the proven principles of DevOps to the world of machine learning, bridging the gap between data science and operations. It helps teams automate workflows, track experiments, and monitor models, ensuring that machine learning systems stay accurate, compliant, and high-performing in real-world environments.
In today’s fast-moving AI landscape, MLOps has become essential for any organization that wants to operationalize ML — transforming one-off experiments into continuously improving, production-ready systems.
In this article, we’ll explore:
- The key drivers behind the rise of MLOps
- Shared challenges between MLOps and DevOps
- The unique complexities that make machine learning operations different
- The core components of a practical MLOps framework — and how platforms like Kiroframe simplify every stage of the process
The driving factors behind the rise of MLOps
In the process of deploying machine learning (ML) models in real-world business environments, the work done by data scientists is just a tiny part of the bigger picture. For ML models to be implemented effectively, data scientists must collaborate closely with various teams, including those from business, engineering, and operations. However, this collaboration can pose organizational challenges, specifically regarding communication, collaboration, and coordination.
A discipline called MLOps (Machine Learning Operations) has emerged to address these challenges. MLOps aims to streamline the deployment process by implementing proven practices. Doing so helps organizations overcome the obstacles when different teams need to work together. Additionally, MLOps brings agility and speed, crucial factors in today’s fast-paced digital landscape.
The actual ML code represents only a tiny part of the entire system in real-world ML systems. It is like a small box in the middle of a much larger and more complex infrastructure needed to support it.
Deploying machine learning (ML) models into real-world business environments is far more complex than writing code in a Jupyter notebook. In production systems, the actual ML model is only a small piece of a much larger puzzle that includes data pipelines, infrastructure, monitoring, versioning, governance, and continuous updates.
Because of this complexity, data scientists cannot operate in isolation. Successful ML deployment requires tight collaboration between business teams, data engineering, IT operations, and ML engineering. In many companies, these teams use different tools, follow different processes, and communicate in different “languages,” which often leads to slowdowns, misalignment, and project failures.
This is where MLOps (Machine Learning Operations) plays a transformative role.
MLOps emerged as a discipline specifically to solve these organizational and technical challenges. By introducing standardized processes, automation, continuous integration, and unified tooling, MLOps helps teams:
- Streamline the movement of models from research to production,
- Improve communication between roles,
- Reduce operational risks,
- and accelerate the overall ML lifecycle.
In today’s fast-paced AI-driven world — where organizations retrain models weekly or even daily — speed, traceability, accountability, and repeatability have become mission-critical. MLOps provides the framework required to keep up.
Many companies have discovered that over 80% of effort in real-world ML systems is not spent on modeling, but on the surrounding infrastructure and operational processes. This makes MLOps essential for turning promising prototypes into reliable, scalable, production-ready systems.
Kiroframe supports this shift by providing unified tools for experiment tracking, dataset management, profiling, and collaborative workflows — helping teams operationalize models faster and with fewer production risks.
The overlapping issues between MLOps and DevOps
Bringing machine learning models into production shares many of the same operational challenges found in traditional software development. For years, DevOps has excelled at addressing these challenges — particularly in areas such as automation, collaboration, and continuous delivery. That’s why many of the proven DevOps principles provide a strong foundation for MLOps.
Just like software systems, ML systems benefit from:
- agile workflows instead of slow, rigid waterfall processes,
- continuous integration and delivery,
- automated testing,
- strong collaboration across teams,
- and clear, repeatable pipelines.
However, ML introduces additional complexities — such as data dependencies, experiment tracking, retraining cycles, and drift — that require ML-specific extensions of DevOps principles.
Why DevOps practices matter for MLOps
In many organizations, the journey from an experiment in a notebook to a fully deployed ML model is slow and disjointed. This is often caused by:
- poor handoff between data scientists and operations teams,
- siloed communication,
- unclear responsibilities,
- and a lack of automation.
These gaps lead to delays, misalignment, and costly errors during deployment.
Adopting DevOps best practices inside ML workflows helps unify teams, improve transparency, shorten delivery cycles, and ensure models are consistently prepared for production.
Shared challenges between DevOps and MLOps — and how DevOps practices solve them
Shared challenge | How it appears in ML projects | DevOps practice that solves it | Benefit for MLOps |
Slow, manual deployment | Models take weeks or months to move from research to production due to unclear handoff | CI/CD pipelines | Automated building, testing, and deployment of ML models |
Poor communication across teams | Data scientists, ML engineers, and Ops work in silos; the final model becomes a “black box” to other teams | Agile methodology & cross-functional sprints | Early feedback, shared visibility, and faster iteration cycles |
Lack of reproducibility | Experiments can’t be recreated; different environments lead to inconsistent results | Version control (Git), IaC, containerization | Consistent ML environments, repeatable experiments, stable deployments |
Unpredictable failures | Late detection of issues related to data quality, code changes, or pipeline errors | Continuous testing & monitoring | Early detection of problems, fewer surprises at deployment |
Complex multi-step processes | ML pipelines involve many moving parts: data, code, artifacts, experiments | Automated workflows & pipeline orchestration | Standardized processes reduce human error and speed up delivery |
DevOps-driven solutions for common MLOps pain points
DevOps methodologies address several MLOps bottlenecks:
- CI/CD helps deliver ML updates reliably by ensuring that each model, feature extraction script, or pipeline component is tested and validated before deployment.
- Agile practices break down ML workflows into smaller, manageable sprints, giving all teams visibility into progress.
- Reproducible environments (e.g., Docker, Infrastructure as Code, IaC) reduce inconsistencies and speed up iteration.
- Automated pipelines eliminate manual steps, minimizing deployment risks and shortening time-to-production.
The unique challenges in MLOps compared to DevOps
MLOps, often called the DevOps of machine learning, aims to address the unique challenges faced in ML. While MLOps shares some similarities with traditional software engineering practices, distinct aspects of ML require specialized solutions. One of these challenges revolves around the role of data. In standard software, developers write code that follows fixed logic and rules. However, data scientists craft code in machine learning that utilizes parameters to solve specific business problems. These parameter values are derived from data, often using techniques like gradient descent. What makes it interesting is that these parameter values can change with different versions of the data, subsequently altering the code’s behavior. In other words, the data is as important as the code in shaping the output.
Moreover, the data and the code can change independently, adding complexity. This creates a layered complexity around data, which needs to be carefully defined and tracked alongside the model code as an intrinsic part of the ML software. MLOps platforms play a crucial role in managing these intricacies and ensuring that the code and the data are properly handled throughout the ML lifecycle.
Although MLOps borrows many principles from DevOps, machine learning introduces a distinct layer of complexity that doesn’t exist in traditional software engineering. In a typical software system, developers write deterministic code — the logic is fixed, and the behavior changes only when the code changes.
In machine learning, however, the “logic” of the system comes from data-driven parameters, not static rules. These parameters — weights, embeddings, decision boundaries, probability thresholds — are learned from data, meaning that new data can change model behavior even when the code remains the same.
This leads to several ML-specific challenges:
Data becomes as essential as code – and often changes more frequently.
- Model behavior evolves in response to new data distributions.
- Code, data, and hyperparameters are all independent variables — each can change separately, creating exponential complexity.
- ML workflows require continuous experimentation, not just linear development.
- Models degrade in production due to drift, noise, bias, or changes in the downstream system.
Because of this, MLOps platforms must manage not only code but also data pipelines, feature stores, model artifacts, metrics, hyperparameters, evaluation workflows, and retraining cycles.
Machine Learning challenges that require MLOps
ML-specific challenge | Why it happens | What makes it different from DevOps |
1. Managing data & hyperparameter versions | Model output depends on changing data, code, and hyperparameters | Software versioning tracks only code — ML requires additional versioning for datasets, features & hyperparameters |
2. Supporting iterative experimentation | ML development is nonlinear & experiment-driven | DevOps optimizes deterministic code — ML requires tracking lineage of runs, metrics, artifacts |
3. Testing ML systems | Data drift, preprocessing, and fairness must be validated continuously | Testing is not only unit tests — ML requires validation of datasets, labels, model metrics, and biases |
4. Security & access control | Models power multiple downstream systems that developers may not control | ML outputs (predictions) must be protected as sensitive assets |
5. Continuous monitoring | Models degrade over time due to drift & changing data distributions | Software monitoring focuses on uptime — ML monitoring focuses on prediction quality |
6. Infrastructure demands | ML training requires GPUs/TPUs and scalable compute | Traditional apps rarely require large-scale distributed GPU training |
Detailed breakdown of MLOps-specific challenges
Managing data and hyperparameter versions
In ML, small changes in input data or hyperparameters can completely transform a model’s behavior. Unlike traditional software, where versioning focuses on code, ML requires synchronized version control for data, features, code, and hyperparameters.
This is especially challenging with large, unstructured datasets (such as images, text, and audio) where classical Git-style tools are insufficient.
Supporting iterative development and experimentation
Machine learning is inherently exploratory: dozens or hundreds of model versions may be trained before finding the best one. Each experiment has its own:
dataset version
code version
hyperparameters
metrics
artifacts
MLOps platforms track this entire lineage, allowing teams to reproduce, compare, and optimize results without guesswork.
ML-specific testing
Testing ML systems requires far more than unit and integration tests:
Data validation: ensure new data follows the same schema and statistical properties.
Preprocessing validation: enable training and serving pipelines to match.
Algorithm validation involves evaluating accuracy, fairness, stability, and explainability.
The goal is to detect problems early before they propagate into production.
Security in ML pipelines
Because ML outputs are consumed by multiple downstream systems — fraud detection engines, content filters, and recommendation services — rigorous security is essential.
Access control must protect:
model artifacts
sensitive datasets
inference APIs
prediction logs
Without proper governance, models become attack surfaces (e.g., data poisoning, prompt injection, adversarial attacks).
Production monitoring
Once deployed, models interact with constantly changing real-world data. Monitoring must detect:
covariate shift (input distribution changes)
label shift
performance degradation
model decay
abnormal patterns in predictions
Without continuous monitoring, ML models silently fail — far more dangerously than traditional software.
Infrastructure requirements
ML workloads often require:
distributed training
GPU/TPU clusters
high-throughput data pipelines
autoscaling inference endpoints
This infrastructure complexity goes beyond typical DevOps needs and must be automated to stay efficient.
The integral parts of a modern MLOps structure
An effective MLOps framework organizes the entire machine learning lifecycle into a sequence of interconnected stages. Each stage helps ensure that machine learning solutions are reliable, scalable, and ready for deployment in real-world applications. Below is a more precise, richer, and more complete breakdown of how these parts fit together in today’s ML ecosystem.
Discovery & problem framing
The lifecycle begins with business teams and data scientists defining the problem the model needs to solve.
This stage focuses on:
- Capturing business goals and constraints
- Selecting the right ML approach (classification, forecasting, NLP, etc.)
- Identifying KPIs and success metrics
- Understanding the data sources available
- Assessing feasibility, risks, and expected impact
Precise problem framing reduces the number of failed experiments and ensures the ML initiative aligns with real business value.
Data engineering & data preparation
Once the use case is defined, data engineers gather and prepare the datasets needed for training and evaluation.
This includes:
- Ingesting data from multiple systems (databases, APIs, logs, sensors, etc.)
- Performing data cleaning, transformation, normalization, and feature extraction
- Building automated pipelines to ensure consistent preprocessing
- Validating schemas, detecting anomalies, and checking statistical properties
- Implementing dataset versioning and tracking lineage
- Creating or maintaining feature stores for consistent training/serving
High-quality, well-structured data is the foundation of every reliable ML model.
Machine Learning pipeline & experimentation
After the data is prepared, teams build a reproducible ML pipeline that supports experimentation, training, tuning, and evaluation.
A modern ML pipeline includes:
- Automated feature engineering
- Training workflows that handle multiple hyperparameter configurations
- Built-in experiment tracking (metrics, parameters, artifacts)
- Version control for models, datasets, and code
- Continuous integration for retraining and validation
- Tools to compare models and select the best candidates
The goal is to transition from a “notebook prototype” to a structured, testable, and repeatable workflow that is easy to optimize over time.
Production deployment
Once a model is validated, it is deployed. This phase focuses on ensuring the model can reliably and securely serve predictions.
Deployment may involve:
- Batch inference, online inference, or streaming architectures
- Containerization (Docker) and orchestration (Kubernetes)
- Implementing rollback strategies and versioned inference endpoints
- Securing model APIs and managing access controls
- Ensuring compatibility with existing systems or microservices
The goal is to deliver a production-ready, scalable model that integrates seamlessly with the business environment.
Production monitoring & operational management
Deployment isn’t the end — it’s the beginning of the most extended and most critical phase in the ML lifecycle.
Ongoing monitoring includes:
- Tracking prediction quality over time
- Monitoring data drift, concept drift, and outliers
- Detecting model decay as new patterns emerge
- Comparing new data distributions to training data
- Logging feature values and performance metrics
- Monitoring infrastructure performance (latency, GPU/CPU usage)
- Triggering automated retraining workflows when thresholds are exceeded
Continuous monitoring ensures that models remain accurate, fair, and reliable, even as real-world conditions evolve.
Iteration, retraining & continuous improvement
MLOps is not a linear process — it is a continuous loop.
When monitoring flagged issues, teams revisit earlier stages:
- Acquire updated data
- Refine preprocessing or features
- Tune hyperparameters
- Try alternative architectures
- Deploy new versions
This adaptive cycle turns machine learning into a living system, not a one-time project.