Sep 6, 2025
How MLOps Differs from DevOps and Why It Matters
You deploy a recommendation model using your standard web service playbook—merge, CI/CD, production—only to watch click-through rates nosedive weeks later. Nothing crashed. API latency looks perfect. Yet your model's relevance silently eroded as user behavior shifted.
DevOps provides the foundation—automation, infrastructure as code, rapid releases—but models introduce unpredictability and heavy dependence on shifting data. Your code freezes while predictions decay.
That's why MLOps practices emerged to version data alongside code, automate training cycles, and track statistical performance beyond basic service health.
DevOps vs MLOps compared
At first glance, DevOps and MLOps pipelines look nearly identical—both built on automation, version control, and continuous delivery.
This illusion ends when a model hits production, and you discover accuracy can collapse while the service runs perfectly. This gap stems from fundamentally different artifacts, triggers, and feedback loops that each discipline handles.
Dimension | DevOps | MLOps |
|---|---|---|
Artifacts managed | Code & configs | Code, datasets, features, models, experiments |
Pipeline stages | CI/CD | CI/CD/CT (continuous training) |
Monitoring focus | Uptime, latency, errors | Data & concept drift, model metrics, bias plus standard SRE signals |
Testing style | Deterministic unit/integration tests | Statistical validation, fairness, slice analysis |
Core team | Developers + operations | Data scientists, ML engineers, data engineers + operations |
These differences trace back to machine learning's unpredictability, data dependency, and need for constant evolution. Let's unpack how these traits reshape versioning, workflow logic, monitoring, and team dynamics.
Artifacts and versioning
When working with traditional DevOps, you'll track source code, configuration files and container images. With machine learning operations, your artifact list expands dramatically.
You need to capture the exact dataset snapshot, feature transformations, model weights, hyperparameters, experiment metrics and training environment to reproduce a result. Miss any piece, and you'll find troubleshooting nearly impossible when accuracy drops.
Since datasets typically dwarf typical repos, you'll benefit from tools like DVC for data snapshots, MLflow model registries and feature stores to maintain complete lineage.
Each model version becomes a comprehensive package: data hash, code commit, hyperparameter set and evaluation metrics. This detail helps you satisfy compliance audits and quickly roll back to a stable state when drift appears.
Workflow complexity and retraining needs
A classic DevOps pipeline follows a predictable path: code → build → test → deploy. Machine learning adds an entirely new feedback loop.
You'll start with data ingestion, create features, train and evaluate multiple candidates, register the winner, deploy it, monitor live traffic and—crucially—trigger retraining when performance dips.
Continuous training (CT) pipelines respond to signals unrelated to code commits: data drift thresholds, new labels, seasonal patterns or scheduled refreshes.
An identical training code can produce a worse model tomorrow because your underlying data has changed. By implementing automated CT, you allow pipelines, not bleary-eyed engineers, to decide when retraining makes sense.
Monitoring requirements
Uptime dashboards won't tell you that your recommendation engine started serving irrelevant items. Beyond response time and error rates, you need ongoing checks on input distributions, feature quality and prediction accuracy.
Data drift detection identifies shifts in incoming features; concept drift tests whether input-output relationships still hold once delayed ground-truth labels arrive.
You can implement practical approaches, including statistical divergence tests, champion-challenger comparisons and slice-based performance analysis.
When alerts trigger, they feed directly into your CT pipeline, closing the loop between observation and action. Without these ML-specific metrics, your models can silently fail while services appear healthy.
Team dynamics and cross-functional roles
Traditional DevOps unites developers and operators; machine learning operations expands this circle. Data scientists explore hypotheses and define performance thresholds. ML engineers convert notebooks into reproducible pipelines and manage GPU-intensive infrastructure.
Data engineers ensure features arrive fresh and consistent. Operations still guards reliability, but now collaborates on drift incidents and model rollbacks.
Decision rights also shift in your organization. Promoting a model isn't just an ops approval—it depends on whether statistical metrics beat the current champion.
Model review boards, shared incident channels, and versioned feature stores become your everyday tools. These cross-functional patterns ensure experimental creativity coexists with production stability.
Similarities between DevOps and MLOps practices
Despite their differences, DevOps and MLOps share a common foundation built on automation, collaboration, and the drive for continuous improvement.
Both aim to shorten development cycles while maintaining high quality and reliability. In each discipline, these principles are expressed through shared practices that form the operational backbone of modern technology delivery.
Shared foundations
Automation sits at the heart of both approaches. In DevOps, this means automating build, test, and deployment processes to reduce manual work and human error. In MLOps, the automation extends beyond software builds to include data ingestion, feature engineering, model training, and validation.
In both cases, automation improves speed and repeatability, enabling teams to focus on higher-value tasks instead of repetitive setup work.
Collaboration is another common pillar. DevOps brought developers and operations teams together to break down silos that caused slow releases and misaligned priorities.
MLOps expands this collaboration to include data scientists, data engineers, and ML engineers, but the spirit remains the same. Cross-functional teams align around shared goals, communicate openly, and work from a unified set of tools and metrics.
Continuous improvement
Both disciplines operate on the belief that release cycles should be short and feedback should be rapid. In DevOps, continuous delivery (CD) ensures that every code change can be pushed to production quickly and safely.
In MLOps, the equivalent is continuous training (CT), which retrains and redeploys models whenever new data or performance signals dictate.
In both cases, the pipeline is designed to accept change as a constant, not an exception.
Use of CI/CD and Infrastructure as Code
CI/CD pipelines form the skeleton for both DevOps and MLOps workflows. They ensure that changes are tested, validated, and promoted through environments with minimal friction.
Infrastructure as Code (IaC) enables teams in both fields to define infrastructure in version-controlled templates. This creates reproducible environments for application servers in DevOps and GPU-powered training clusters in MLOps alike.
Emphasis on monitoring and efficient delivery
In both practices, monitoring is treated as an active discipline rather than a passive safeguard. For DevOps, this might focus on service uptime, latency, and resource consumption.
In MLOps, it includes these same metrics plus model-specific signals like data drift and accuracy over time. The shared goal is to detect issues early, resolve them quickly, and keep delivering value to users without interruption.
When to use MLOps vs DevOps
Choosing between MLOps and DevOps is less about picking one over the other and more about matching the approach to the type of system you’re running.
Use DevOps when you’re deploying traditional applications where behavior is driven entirely by code. These systems are relatively stable once tested and shipped, and your monitoring focuses on uptime, latency, and error rates.
Use MLOps when your application includes machine learning models that rely on constantly changing data. In these cases, code is only one part of the equation — model performance can drift over time even if the code remains untouched.
If your product roadmap includes features like recommendation engines, predictive analytics, fraud detection, or natural language processing, an MLOps approach will give you the tooling and processes needed to track model quality, retrain when data changes, and keep predictions relevant.
In many organizations, DevOps remains the backbone for the application infrastructure, while MLOps layers on top to handle the lifecycle of machine learning components.
How to bridge the gap between MLOps and DevOps
While MLOps and DevOps focus on different challenges, the most resilient production systems combine strengths from both. DevOps ensures that infrastructure is stable, automated, and scalable. MLOps ensures that models are accurate, fair, and continuously improving.
Bridging the gap starts with unifying pipelines and tooling. CI/CD frameworks should be extended to include Continuous Training (CT) triggers, automated model validation, and drift detection.
Version control systems should store not just code, but datasets, feature definitions, and model artifacts. Monitoring platforms should provide a single pane of glass for both service health and model performance.
By integrating into both your DevOps and MLOps workflows, Galileo gives teams real-time visibility into how models behave in production, tracks performance trends over time, and alerts you before quality issues impact users.
Instead of managing two disconnected monitoring stacks, you can maintain one seamless process — ensuring both your infrastructure and your models remain reliable, efficient, and aligned with business goals.
Strengthen your MLOps workflows with Galileo
Even with a well-built MLOps stack, teams often discover issues too late — when users are already affected. Silent model drift, unseen data quality problems, and fairness gaps can all slip past traditional monitoring, eroding trust and performance before anyone notices.
Galileo provides the observability and evaluation layer that strengthens MLOps workflows.
Track model performance in production beyond uptime metrics, catching drift, bias, and data quality issues before they cause failures.
Integrate with CI/CD and CT pipelines to automatically validate models against real-world traffic patterns and segment-level metrics.
Maintain full version lineage of datasets, features, and models for audit and compliance needs.
Use dashboards and alerts to keep all stakeholders informed, from data scientists to operations.
Start with Galileo today to make your machine learning operations more reliable, efficient, and transparent.
You deploy a recommendation model using your standard web service playbook—merge, CI/CD, production—only to watch click-through rates nosedive weeks later. Nothing crashed. API latency looks perfect. Yet your model's relevance silently eroded as user behavior shifted.
DevOps provides the foundation—automation, infrastructure as code, rapid releases—but models introduce unpredictability and heavy dependence on shifting data. Your code freezes while predictions decay.
That's why MLOps practices emerged to version data alongside code, automate training cycles, and track statistical performance beyond basic service health.
DevOps vs MLOps compared
At first glance, DevOps and MLOps pipelines look nearly identical—both built on automation, version control, and continuous delivery.
This illusion ends when a model hits production, and you discover accuracy can collapse while the service runs perfectly. This gap stems from fundamentally different artifacts, triggers, and feedback loops that each discipline handles.
Dimension | DevOps | MLOps |
|---|---|---|
Artifacts managed | Code & configs | Code, datasets, features, models, experiments |
Pipeline stages | CI/CD | CI/CD/CT (continuous training) |
Monitoring focus | Uptime, latency, errors | Data & concept drift, model metrics, bias plus standard SRE signals |
Testing style | Deterministic unit/integration tests | Statistical validation, fairness, slice analysis |
Core team | Developers + operations | Data scientists, ML engineers, data engineers + operations |
These differences trace back to machine learning's unpredictability, data dependency, and need for constant evolution. Let's unpack how these traits reshape versioning, workflow logic, monitoring, and team dynamics.
Artifacts and versioning
When working with traditional DevOps, you'll track source code, configuration files and container images. With machine learning operations, your artifact list expands dramatically.
You need to capture the exact dataset snapshot, feature transformations, model weights, hyperparameters, experiment metrics and training environment to reproduce a result. Miss any piece, and you'll find troubleshooting nearly impossible when accuracy drops.
Since datasets typically dwarf typical repos, you'll benefit from tools like DVC for data snapshots, MLflow model registries and feature stores to maintain complete lineage.
Each model version becomes a comprehensive package: data hash, code commit, hyperparameter set and evaluation metrics. This detail helps you satisfy compliance audits and quickly roll back to a stable state when drift appears.
Workflow complexity and retraining needs
A classic DevOps pipeline follows a predictable path: code → build → test → deploy. Machine learning adds an entirely new feedback loop.
You'll start with data ingestion, create features, train and evaluate multiple candidates, register the winner, deploy it, monitor live traffic and—crucially—trigger retraining when performance dips.
Continuous training (CT) pipelines respond to signals unrelated to code commits: data drift thresholds, new labels, seasonal patterns or scheduled refreshes.
An identical training code can produce a worse model tomorrow because your underlying data has changed. By implementing automated CT, you allow pipelines, not bleary-eyed engineers, to decide when retraining makes sense.
Monitoring requirements
Uptime dashboards won't tell you that your recommendation engine started serving irrelevant items. Beyond response time and error rates, you need ongoing checks on input distributions, feature quality and prediction accuracy.
Data drift detection identifies shifts in incoming features; concept drift tests whether input-output relationships still hold once delayed ground-truth labels arrive.
You can implement practical approaches, including statistical divergence tests, champion-challenger comparisons and slice-based performance analysis.
When alerts trigger, they feed directly into your CT pipeline, closing the loop between observation and action. Without these ML-specific metrics, your models can silently fail while services appear healthy.
Team dynamics and cross-functional roles
Traditional DevOps unites developers and operators; machine learning operations expands this circle. Data scientists explore hypotheses and define performance thresholds. ML engineers convert notebooks into reproducible pipelines and manage GPU-intensive infrastructure.
Data engineers ensure features arrive fresh and consistent. Operations still guards reliability, but now collaborates on drift incidents and model rollbacks.
Decision rights also shift in your organization. Promoting a model isn't just an ops approval—it depends on whether statistical metrics beat the current champion.
Model review boards, shared incident channels, and versioned feature stores become your everyday tools. These cross-functional patterns ensure experimental creativity coexists with production stability.
Similarities between DevOps and MLOps practices
Despite their differences, DevOps and MLOps share a common foundation built on automation, collaboration, and the drive for continuous improvement.
Both aim to shorten development cycles while maintaining high quality and reliability. In each discipline, these principles are expressed through shared practices that form the operational backbone of modern technology delivery.
Shared foundations
Automation sits at the heart of both approaches. In DevOps, this means automating build, test, and deployment processes to reduce manual work and human error. In MLOps, the automation extends beyond software builds to include data ingestion, feature engineering, model training, and validation.
In both cases, automation improves speed and repeatability, enabling teams to focus on higher-value tasks instead of repetitive setup work.
Collaboration is another common pillar. DevOps brought developers and operations teams together to break down silos that caused slow releases and misaligned priorities.
MLOps expands this collaboration to include data scientists, data engineers, and ML engineers, but the spirit remains the same. Cross-functional teams align around shared goals, communicate openly, and work from a unified set of tools and metrics.
Continuous improvement
Both disciplines operate on the belief that release cycles should be short and feedback should be rapid. In DevOps, continuous delivery (CD) ensures that every code change can be pushed to production quickly and safely.
In MLOps, the equivalent is continuous training (CT), which retrains and redeploys models whenever new data or performance signals dictate.
In both cases, the pipeline is designed to accept change as a constant, not an exception.
Use of CI/CD and Infrastructure as Code
CI/CD pipelines form the skeleton for both DevOps and MLOps workflows. They ensure that changes are tested, validated, and promoted through environments with minimal friction.
Infrastructure as Code (IaC) enables teams in both fields to define infrastructure in version-controlled templates. This creates reproducible environments for application servers in DevOps and GPU-powered training clusters in MLOps alike.
Emphasis on monitoring and efficient delivery
In both practices, monitoring is treated as an active discipline rather than a passive safeguard. For DevOps, this might focus on service uptime, latency, and resource consumption.
In MLOps, it includes these same metrics plus model-specific signals like data drift and accuracy over time. The shared goal is to detect issues early, resolve them quickly, and keep delivering value to users without interruption.
When to use MLOps vs DevOps
Choosing between MLOps and DevOps is less about picking one over the other and more about matching the approach to the type of system you’re running.
Use DevOps when you’re deploying traditional applications where behavior is driven entirely by code. These systems are relatively stable once tested and shipped, and your monitoring focuses on uptime, latency, and error rates.
Use MLOps when your application includes machine learning models that rely on constantly changing data. In these cases, code is only one part of the equation — model performance can drift over time even if the code remains untouched.
If your product roadmap includes features like recommendation engines, predictive analytics, fraud detection, or natural language processing, an MLOps approach will give you the tooling and processes needed to track model quality, retrain when data changes, and keep predictions relevant.
In many organizations, DevOps remains the backbone for the application infrastructure, while MLOps layers on top to handle the lifecycle of machine learning components.
How to bridge the gap between MLOps and DevOps
While MLOps and DevOps focus on different challenges, the most resilient production systems combine strengths from both. DevOps ensures that infrastructure is stable, automated, and scalable. MLOps ensures that models are accurate, fair, and continuously improving.
Bridging the gap starts with unifying pipelines and tooling. CI/CD frameworks should be extended to include Continuous Training (CT) triggers, automated model validation, and drift detection.
Version control systems should store not just code, but datasets, feature definitions, and model artifacts. Monitoring platforms should provide a single pane of glass for both service health and model performance.
By integrating into both your DevOps and MLOps workflows, Galileo gives teams real-time visibility into how models behave in production, tracks performance trends over time, and alerts you before quality issues impact users.
Instead of managing two disconnected monitoring stacks, you can maintain one seamless process — ensuring both your infrastructure and your models remain reliable, efficient, and aligned with business goals.
Strengthen your MLOps workflows with Galileo
Even with a well-built MLOps stack, teams often discover issues too late — when users are already affected. Silent model drift, unseen data quality problems, and fairness gaps can all slip past traditional monitoring, eroding trust and performance before anyone notices.
Galileo provides the observability and evaluation layer that strengthens MLOps workflows.
Track model performance in production beyond uptime metrics, catching drift, bias, and data quality issues before they cause failures.
Integrate with CI/CD and CT pipelines to automatically validate models against real-world traffic patterns and segment-level metrics.
Maintain full version lineage of datasets, features, and models for audit and compliance needs.
Use dashboards and alerts to keep all stakeholders informed, from data scientists to operations.
Start with Galileo today to make your machine learning operations more reliable, efficient, and transparent.
You deploy a recommendation model using your standard web service playbook—merge, CI/CD, production—only to watch click-through rates nosedive weeks later. Nothing crashed. API latency looks perfect. Yet your model's relevance silently eroded as user behavior shifted.
DevOps provides the foundation—automation, infrastructure as code, rapid releases—but models introduce unpredictability and heavy dependence on shifting data. Your code freezes while predictions decay.
That's why MLOps practices emerged to version data alongside code, automate training cycles, and track statistical performance beyond basic service health.
DevOps vs MLOps compared
At first glance, DevOps and MLOps pipelines look nearly identical—both built on automation, version control, and continuous delivery.
This illusion ends when a model hits production, and you discover accuracy can collapse while the service runs perfectly. This gap stems from fundamentally different artifacts, triggers, and feedback loops that each discipline handles.
Dimension | DevOps | MLOps |
|---|---|---|
Artifacts managed | Code & configs | Code, datasets, features, models, experiments |
Pipeline stages | CI/CD | CI/CD/CT (continuous training) |
Monitoring focus | Uptime, latency, errors | Data & concept drift, model metrics, bias plus standard SRE signals |
Testing style | Deterministic unit/integration tests | Statistical validation, fairness, slice analysis |
Core team | Developers + operations | Data scientists, ML engineers, data engineers + operations |
These differences trace back to machine learning's unpredictability, data dependency, and need for constant evolution. Let's unpack how these traits reshape versioning, workflow logic, monitoring, and team dynamics.
Artifacts and versioning
When working with traditional DevOps, you'll track source code, configuration files and container images. With machine learning operations, your artifact list expands dramatically.
You need to capture the exact dataset snapshot, feature transformations, model weights, hyperparameters, experiment metrics and training environment to reproduce a result. Miss any piece, and you'll find troubleshooting nearly impossible when accuracy drops.
Since datasets typically dwarf typical repos, you'll benefit from tools like DVC for data snapshots, MLflow model registries and feature stores to maintain complete lineage.
Each model version becomes a comprehensive package: data hash, code commit, hyperparameter set and evaluation metrics. This detail helps you satisfy compliance audits and quickly roll back to a stable state when drift appears.
Workflow complexity and retraining needs
A classic DevOps pipeline follows a predictable path: code → build → test → deploy. Machine learning adds an entirely new feedback loop.
You'll start with data ingestion, create features, train and evaluate multiple candidates, register the winner, deploy it, monitor live traffic and—crucially—trigger retraining when performance dips.
Continuous training (CT) pipelines respond to signals unrelated to code commits: data drift thresholds, new labels, seasonal patterns or scheduled refreshes.
An identical training code can produce a worse model tomorrow because your underlying data has changed. By implementing automated CT, you allow pipelines, not bleary-eyed engineers, to decide when retraining makes sense.
Monitoring requirements
Uptime dashboards won't tell you that your recommendation engine started serving irrelevant items. Beyond response time and error rates, you need ongoing checks on input distributions, feature quality and prediction accuracy.
Data drift detection identifies shifts in incoming features; concept drift tests whether input-output relationships still hold once delayed ground-truth labels arrive.
You can implement practical approaches, including statistical divergence tests, champion-challenger comparisons and slice-based performance analysis.
When alerts trigger, they feed directly into your CT pipeline, closing the loop between observation and action. Without these ML-specific metrics, your models can silently fail while services appear healthy.
Team dynamics and cross-functional roles
Traditional DevOps unites developers and operators; machine learning operations expands this circle. Data scientists explore hypotheses and define performance thresholds. ML engineers convert notebooks into reproducible pipelines and manage GPU-intensive infrastructure.
Data engineers ensure features arrive fresh and consistent. Operations still guards reliability, but now collaborates on drift incidents and model rollbacks.
Decision rights also shift in your organization. Promoting a model isn't just an ops approval—it depends on whether statistical metrics beat the current champion.
Model review boards, shared incident channels, and versioned feature stores become your everyday tools. These cross-functional patterns ensure experimental creativity coexists with production stability.
Similarities between DevOps and MLOps practices
Despite their differences, DevOps and MLOps share a common foundation built on automation, collaboration, and the drive for continuous improvement.
Both aim to shorten development cycles while maintaining high quality and reliability. In each discipline, these principles are expressed through shared practices that form the operational backbone of modern technology delivery.
Shared foundations
Automation sits at the heart of both approaches. In DevOps, this means automating build, test, and deployment processes to reduce manual work and human error. In MLOps, the automation extends beyond software builds to include data ingestion, feature engineering, model training, and validation.
In both cases, automation improves speed and repeatability, enabling teams to focus on higher-value tasks instead of repetitive setup work.
Collaboration is another common pillar. DevOps brought developers and operations teams together to break down silos that caused slow releases and misaligned priorities.
MLOps expands this collaboration to include data scientists, data engineers, and ML engineers, but the spirit remains the same. Cross-functional teams align around shared goals, communicate openly, and work from a unified set of tools and metrics.
Continuous improvement
Both disciplines operate on the belief that release cycles should be short and feedback should be rapid. In DevOps, continuous delivery (CD) ensures that every code change can be pushed to production quickly and safely.
In MLOps, the equivalent is continuous training (CT), which retrains and redeploys models whenever new data or performance signals dictate.
In both cases, the pipeline is designed to accept change as a constant, not an exception.
Use of CI/CD and Infrastructure as Code
CI/CD pipelines form the skeleton for both DevOps and MLOps workflows. They ensure that changes are tested, validated, and promoted through environments with minimal friction.
Infrastructure as Code (IaC) enables teams in both fields to define infrastructure in version-controlled templates. This creates reproducible environments for application servers in DevOps and GPU-powered training clusters in MLOps alike.
Emphasis on monitoring and efficient delivery
In both practices, monitoring is treated as an active discipline rather than a passive safeguard. For DevOps, this might focus on service uptime, latency, and resource consumption.
In MLOps, it includes these same metrics plus model-specific signals like data drift and accuracy over time. The shared goal is to detect issues early, resolve them quickly, and keep delivering value to users without interruption.
When to use MLOps vs DevOps
Choosing between MLOps and DevOps is less about picking one over the other and more about matching the approach to the type of system you’re running.
Use DevOps when you’re deploying traditional applications where behavior is driven entirely by code. These systems are relatively stable once tested and shipped, and your monitoring focuses on uptime, latency, and error rates.
Use MLOps when your application includes machine learning models that rely on constantly changing data. In these cases, code is only one part of the equation — model performance can drift over time even if the code remains untouched.
If your product roadmap includes features like recommendation engines, predictive analytics, fraud detection, or natural language processing, an MLOps approach will give you the tooling and processes needed to track model quality, retrain when data changes, and keep predictions relevant.
In many organizations, DevOps remains the backbone for the application infrastructure, while MLOps layers on top to handle the lifecycle of machine learning components.
How to bridge the gap between MLOps and DevOps
While MLOps and DevOps focus on different challenges, the most resilient production systems combine strengths from both. DevOps ensures that infrastructure is stable, automated, and scalable. MLOps ensures that models are accurate, fair, and continuously improving.
Bridging the gap starts with unifying pipelines and tooling. CI/CD frameworks should be extended to include Continuous Training (CT) triggers, automated model validation, and drift detection.
Version control systems should store not just code, but datasets, feature definitions, and model artifacts. Monitoring platforms should provide a single pane of glass for both service health and model performance.
By integrating into both your DevOps and MLOps workflows, Galileo gives teams real-time visibility into how models behave in production, tracks performance trends over time, and alerts you before quality issues impact users.
Instead of managing two disconnected monitoring stacks, you can maintain one seamless process — ensuring both your infrastructure and your models remain reliable, efficient, and aligned with business goals.
Strengthen your MLOps workflows with Galileo
Even with a well-built MLOps stack, teams often discover issues too late — when users are already affected. Silent model drift, unseen data quality problems, and fairness gaps can all slip past traditional monitoring, eroding trust and performance before anyone notices.
Galileo provides the observability and evaluation layer that strengthens MLOps workflows.
Track model performance in production beyond uptime metrics, catching drift, bias, and data quality issues before they cause failures.
Integrate with CI/CD and CT pipelines to automatically validate models against real-world traffic patterns and segment-level metrics.
Maintain full version lineage of datasets, features, and models for audit and compliance needs.
Use dashboards and alerts to keep all stakeholders informed, from data scientists to operations.
Start with Galileo today to make your machine learning operations more reliable, efficient, and transparent.
You deploy a recommendation model using your standard web service playbook—merge, CI/CD, production—only to watch click-through rates nosedive weeks later. Nothing crashed. API latency looks perfect. Yet your model's relevance silently eroded as user behavior shifted.
DevOps provides the foundation—automation, infrastructure as code, rapid releases—but models introduce unpredictability and heavy dependence on shifting data. Your code freezes while predictions decay.
That's why MLOps practices emerged to version data alongside code, automate training cycles, and track statistical performance beyond basic service health.
DevOps vs MLOps compared
At first glance, DevOps and MLOps pipelines look nearly identical—both built on automation, version control, and continuous delivery.
This illusion ends when a model hits production, and you discover accuracy can collapse while the service runs perfectly. This gap stems from fundamentally different artifacts, triggers, and feedback loops that each discipline handles.
Dimension | DevOps | MLOps |
|---|---|---|
Artifacts managed | Code & configs | Code, datasets, features, models, experiments |
Pipeline stages | CI/CD | CI/CD/CT (continuous training) |
Monitoring focus | Uptime, latency, errors | Data & concept drift, model metrics, bias plus standard SRE signals |
Testing style | Deterministic unit/integration tests | Statistical validation, fairness, slice analysis |
Core team | Developers + operations | Data scientists, ML engineers, data engineers + operations |
These differences trace back to machine learning's unpredictability, data dependency, and need for constant evolution. Let's unpack how these traits reshape versioning, workflow logic, monitoring, and team dynamics.
Artifacts and versioning
When working with traditional DevOps, you'll track source code, configuration files and container images. With machine learning operations, your artifact list expands dramatically.
You need to capture the exact dataset snapshot, feature transformations, model weights, hyperparameters, experiment metrics and training environment to reproduce a result. Miss any piece, and you'll find troubleshooting nearly impossible when accuracy drops.
Since datasets typically dwarf typical repos, you'll benefit from tools like DVC for data snapshots, MLflow model registries and feature stores to maintain complete lineage.
Each model version becomes a comprehensive package: data hash, code commit, hyperparameter set and evaluation metrics. This detail helps you satisfy compliance audits and quickly roll back to a stable state when drift appears.
Workflow complexity and retraining needs
A classic DevOps pipeline follows a predictable path: code → build → test → deploy. Machine learning adds an entirely new feedback loop.
You'll start with data ingestion, create features, train and evaluate multiple candidates, register the winner, deploy it, monitor live traffic and—crucially—trigger retraining when performance dips.
Continuous training (CT) pipelines respond to signals unrelated to code commits: data drift thresholds, new labels, seasonal patterns or scheduled refreshes.
An identical training code can produce a worse model tomorrow because your underlying data has changed. By implementing automated CT, you allow pipelines, not bleary-eyed engineers, to decide when retraining makes sense.
Monitoring requirements
Uptime dashboards won't tell you that your recommendation engine started serving irrelevant items. Beyond response time and error rates, you need ongoing checks on input distributions, feature quality and prediction accuracy.
Data drift detection identifies shifts in incoming features; concept drift tests whether input-output relationships still hold once delayed ground-truth labels arrive.
You can implement practical approaches, including statistical divergence tests, champion-challenger comparisons and slice-based performance analysis.
When alerts trigger, they feed directly into your CT pipeline, closing the loop between observation and action. Without these ML-specific metrics, your models can silently fail while services appear healthy.
Team dynamics and cross-functional roles
Traditional DevOps unites developers and operators; machine learning operations expands this circle. Data scientists explore hypotheses and define performance thresholds. ML engineers convert notebooks into reproducible pipelines and manage GPU-intensive infrastructure.
Data engineers ensure features arrive fresh and consistent. Operations still guards reliability, but now collaborates on drift incidents and model rollbacks.
Decision rights also shift in your organization. Promoting a model isn't just an ops approval—it depends on whether statistical metrics beat the current champion.
Model review boards, shared incident channels, and versioned feature stores become your everyday tools. These cross-functional patterns ensure experimental creativity coexists with production stability.
Similarities between DevOps and MLOps practices
Despite their differences, DevOps and MLOps share a common foundation built on automation, collaboration, and the drive for continuous improvement.
Both aim to shorten development cycles while maintaining high quality and reliability. In each discipline, these principles are expressed through shared practices that form the operational backbone of modern technology delivery.
Shared foundations
Automation sits at the heart of both approaches. In DevOps, this means automating build, test, and deployment processes to reduce manual work and human error. In MLOps, the automation extends beyond software builds to include data ingestion, feature engineering, model training, and validation.
In both cases, automation improves speed and repeatability, enabling teams to focus on higher-value tasks instead of repetitive setup work.
Collaboration is another common pillar. DevOps brought developers and operations teams together to break down silos that caused slow releases and misaligned priorities.
MLOps expands this collaboration to include data scientists, data engineers, and ML engineers, but the spirit remains the same. Cross-functional teams align around shared goals, communicate openly, and work from a unified set of tools and metrics.
Continuous improvement
Both disciplines operate on the belief that release cycles should be short and feedback should be rapid. In DevOps, continuous delivery (CD) ensures that every code change can be pushed to production quickly and safely.
In MLOps, the equivalent is continuous training (CT), which retrains and redeploys models whenever new data or performance signals dictate.
In both cases, the pipeline is designed to accept change as a constant, not an exception.
Use of CI/CD and Infrastructure as Code
CI/CD pipelines form the skeleton for both DevOps and MLOps workflows. They ensure that changes are tested, validated, and promoted through environments with minimal friction.
Infrastructure as Code (IaC) enables teams in both fields to define infrastructure in version-controlled templates. This creates reproducible environments for application servers in DevOps and GPU-powered training clusters in MLOps alike.
Emphasis on monitoring and efficient delivery
In both practices, monitoring is treated as an active discipline rather than a passive safeguard. For DevOps, this might focus on service uptime, latency, and resource consumption.
In MLOps, it includes these same metrics plus model-specific signals like data drift and accuracy over time. The shared goal is to detect issues early, resolve them quickly, and keep delivering value to users without interruption.
When to use MLOps vs DevOps
Choosing between MLOps and DevOps is less about picking one over the other and more about matching the approach to the type of system you’re running.
Use DevOps when you’re deploying traditional applications where behavior is driven entirely by code. These systems are relatively stable once tested and shipped, and your monitoring focuses on uptime, latency, and error rates.
Use MLOps when your application includes machine learning models that rely on constantly changing data. In these cases, code is only one part of the equation — model performance can drift over time even if the code remains untouched.
If your product roadmap includes features like recommendation engines, predictive analytics, fraud detection, or natural language processing, an MLOps approach will give you the tooling and processes needed to track model quality, retrain when data changes, and keep predictions relevant.
In many organizations, DevOps remains the backbone for the application infrastructure, while MLOps layers on top to handle the lifecycle of machine learning components.
How to bridge the gap between MLOps and DevOps
While MLOps and DevOps focus on different challenges, the most resilient production systems combine strengths from both. DevOps ensures that infrastructure is stable, automated, and scalable. MLOps ensures that models are accurate, fair, and continuously improving.
Bridging the gap starts with unifying pipelines and tooling. CI/CD frameworks should be extended to include Continuous Training (CT) triggers, automated model validation, and drift detection.
Version control systems should store not just code, but datasets, feature definitions, and model artifacts. Monitoring platforms should provide a single pane of glass for both service health and model performance.
By integrating into both your DevOps and MLOps workflows, Galileo gives teams real-time visibility into how models behave in production, tracks performance trends over time, and alerts you before quality issues impact users.
Instead of managing two disconnected monitoring stacks, you can maintain one seamless process — ensuring both your infrastructure and your models remain reliable, efficient, and aligned with business goals.
Strengthen your MLOps workflows with Galileo
Even with a well-built MLOps stack, teams often discover issues too late — when users are already affected. Silent model drift, unseen data quality problems, and fairness gaps can all slip past traditional monitoring, eroding trust and performance before anyone notices.
Galileo provides the observability and evaluation layer that strengthens MLOps workflows.
Track model performance in production beyond uptime metrics, catching drift, bias, and data quality issues before they cause failures.
Integrate with CI/CD and CT pipelines to automatically validate models against real-world traffic patterns and segment-level metrics.
Maintain full version lineage of datasets, features, and models for audit and compliance needs.
Use dashboards and alerts to keep all stakeholders informed, from data scientists to operations.
Start with Galileo today to make your machine learning operations more reliable, efficient, and transparent.
Conor Bronsdon