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MLOps vs DevOps: Differences, Overlaps, and Use Cases
Increasingly, companies are eager to incorporate Machine Learning (ML) into their products or enhance their processes using ML. Consequently, understanding how to navigate its complexities alongside traditional software is more important than ever.
DevOps was introduced to deal with challenges regarding software development. It aims to break down silos between software development teams and operation teams, leading to faster deployment of new features and less painful releases of these features into the production environment.
MLOps builds upon these principles but addresses unique challenges in ML, such as massive and quickly evolving datasets, models that need to be tracked and versioned, and monitoring to address challenges like data drift or concept drift.
While both disciplines share common principles, they heavily differ in their focus, workflows, and tools.
In this article, we’ll explore DevOps and MLOps, highlighting their similarities and differences and when to use which approach. We’ll also look into typical workflows and what tools to use for the different stages of these workflows.
Let’s get started!
What is DevOps?
DevOps is a software engineering methodology invented to speed up the software development and delivery process. The main aim is to improve the collaboration between development and operations teams. It bridges the gap between these traditionally siloed functions and enables faster, more reliable software delivery.
So, DevOps wants to create a culture of collaboration and automate as much as possible to improve overall efficiency and the speed with which new software features can be delivered.
Nowadays, large companies like Meta, Amazon, and many more can deliver new software features daily because of DevOps.
If you’d like to familiarize yourself with this area, I recommend the Introduction to DevOps course.
The main goals of DevOps are:
- Faster software development cycles: Shorten the time between releasing and deploying a new feature to achieve more rapid release cycles while maintaining stability.
- Continuous integration and delivery (CI/CD): Automate code integration, testing, and deployment pipelines to improve delivery.
- Improved collaboration: Encourage close communication between software development teams and operations teams.
- Operational efficiency: Reduce manual tasks, minimize errors, and increase productivity through automation.
DevOps has the following core components:
- CI/CD pipelines:
- Automated testing, building, and deployment of code.
- Faster feedback loops for developers.
- Reduced errors and increased stability.
- Infrastructure as Code (IaC):
- Manage infrastructure through code rather than manual processes.
- Ensure consistency and stability.
- New infrastructure can be set up within seconds and without manual effort.
- Monitoring and observability:
- Track system performance and application health in real time.
- Identify and resolve issues as early as possible.
There are industry-standard tools available that help implement the core components of DevOps. Here are some of the most popular ones:
- CI/CD tools: Jenkins, GitLab CI/CD, CircleCI.
- IaC tools: Terraform, Ansible, Chef, Puppet.
- Containerization and orchestration: Docker, Kubernetes.
- Monitoring tools: Prometheus, Grafana, Datadog.
What is MLOps?
MLOps extends the principles of DevOps while sharing the common goal of automating processes. MLOps consists of practices designed to continuously, reliably, and efficiently deploy and maintain machine learning in production.
The critical part here is that it focuses on machine learning in production, not on models trained on local machines for small experiments.
MLOps can automate and streamline all stages of the machine learning project lifecycle.
Machine learning project lifecycle. Image by Author
But why do we need MLOps in the first place? Why should we invest the additional effort to set up clean workflows and processes? Let’s be honest: It is a lot of extra effort initially, and developers might be tempted to ignore it to deploy a model into production quickly. However, this approach introduces risk.
As with traditional software development, ignoring clean processes and best practices early leads to growing complexity and reduced maintainability over time.
This problem is even more significant in ML systems due to the unique challenges of managing data dependencies, evolving models, and feedback loops.
As highlighted in the influential paper “Hidden Technical Debt in Machine Learning Systems,” machine learning systems accumulate technical debt in ways that traditional software does not.
So, without MLOps principles, the cost of refactoring an unmanageable ML system later often exceeds the effort required to build a robust workflow.
I recommend the excellent MLOps Concepts course if you’d like to learn more about this area.
The main goals of MLOps are:
- Automation of the ML lifecycle: Simplify processes such as data collection, model training, deployment, and monitoring and reduce manual effort.
- Reproducibility and consistency: Ensure that experiments and workflows are repeatable, leading to reliable results.
- Collaboration: Support communication between data scientists, ML engineers, data engineers, and operations teams.
- Scalability: Enable machine learning models to handle production workloads and adapt to growing data and usage demands.
- Addressing ML-specific challenges: Handle changing data distributions, large datasets, and the need for model retraining.
Let’s now come to the core components of MLOps:
- Model training and retraining pipelines: Automating the training process involves:
- Data preprocessing and feature engineering.
- Model validation and performance tracking.
- Retraining models to adapt to new data as required.
- Model and data versioning and experiment tracking:
- Version control ML models and training data to document changes over time and allow for rollbacks.
- Monitor hyperparameters, metrics, and all outcomes of a training run using experiment tracking.
- Data pipeline orchestration:
- Automate data preparation, transformation, and validation. Ensure that they are reliable and repeatable.
- Address challenges with changing datasets.
- Monitoring and maintenance:
- Continuously track the performance of models in production to detect issues like model drift or degradation as early as possible.
- Automatically trigger model retraining and automate model training to quickly update the models as required.
- Infrastructure as Code (IaC):
- Use tools like Kubernetes and Terraform to provision and manage a scalable infrastructure for your ML workloads.
The field of MLOps is still evolving rapidly. However, there are already industry standards for implementing the core components available:
- Experiment tracking: MLFlow or Weights and Biases.
- Data versioning: DVC (Data Version Control).
- Pipeline orchestration: Apache Airflow, Kubeflow, or Dagster.
- Model deployment: TensorFlow Serving, Seldon Core, Amazon SageMaker.
- Monitoring: Prometheus, Grafana, WhyLabs.
- Infrastructure as Code: Kubernetes, Docker, Terraform.
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Differences Between MLOps and DevOps
While MLOPs build upon DevOps and, therefore, have many areas of overlap, they differ in focus, artifacts managed, lifecycles, tools, and team structures. This is mainly due to machine learning workflows' unique challenges and requirements.
In the next subchapters, I’ll focus on the differences between both disciplines in specific areas.
Focus and scope
DevOps focuses on software applications and ensures efficient development, testing, deployment, and maintenance. DevOps is code-centric.
MLOps focuses on machine learning models and data workflows, addressing data preparation challenges, model training, model deployment, and monitoring.
For MLOps, it is essential that the complete end-to-end workflow, from data preparation to model training, is tracked and can be reproduced. MLOps is, therefore, data- and model-centric.
Artifacts managed
While DevOps mainly manages software artifacts like source code, binaries, and configuration files, MLOps manages ML-specific artifacts like models, datasets, features, and experiment configuration and results.
DevOps artifacts are static, while MLOps artifacts are dynamic as they evolve through retraining and data changes.
So, the key difference here is that MLOps must manage the lifecycle of both software code and data-driven artifacts. This is also why MLOps is built upon DevOps but extends it in certain areas.
Lifecycles
DevOps focuses on stages like build, test, deploy, and monitor. Once an application is in production, its behavior is entirely predictable.
MLOps focuses on the same stages as DevOps but incorporates additional stages like data preprocessing, feature engineering, model training and retraining, and data or concept drift monitoring.
The application’s behavior is less predictable and is highly iterative, as the model needs frequent updates to adapt to changes in the dataset.
So, the key difference is that MLOps lifecycles are data-driven, while DevOps lifecycles are more code-focused.
Tools and pipelines
DevOps is code-focused and relies on CI/CD pipelines to automate the build, test, and deploy stages. Tools like Gitlab CI/CD, Jenkins, or Terraform can be used for this.
MLOps, in turn, needs to build pipelines for data processing, model training, and model deployment. Therefore, it requires tools and pipelines to create data and training workflows, which can be done using tools like MLFlow, Kubeflow, Airflow, or SageMaker.
In some cases, MLOps pipelines also need support for unique infrastructure, like GPUs, which is not required for DevOps pipelines.
Team structure
DevOps involves collaboration between software and operations teams. There is also a clear separation of concerns, as software engineers will focus on coding, while operations teams will focus on the infrastructure part.
MLOps projects involve data scientists, ML engineers, data engineers, and operations teams. On top of that, these projects also need domain experts if the data scientist is not the primary domain expert.
This means that both disciplines need different organizational structures in companies.
Areas of Overlap Between MLOps and DevOps
Now that we’ve discussed the differences, we can focus on the overlap areas in both disciplines.
To repeat the most important part: MLOps builds upon DevOps while extending it to adapt to the unique challenges of machine learning. Both disciplines focus on automating workflows, improving collaboration, and ensuring quality and scalability.
CI/CD pipelines
Both disciplines automate their workflows using CI/CD pipelines. MLOps and DevOps must manage code that needs to be built, tested, and deployed.
MLOps extend that to include data validation, model training, and deployment pipelines.
However, both disciplines generally use CI/CD to simplify the shift from development to production.
Infrastructure management
Both use Infrastructure as Code (IaC) to provision, manage automatically, and scale infrastructure without manual steps. Tools like Docker, Terraform, and Kubernetes are broadly used in both disciplines.
Both disciplines include the provisioning of application servers, databases, and APIs. MLOps only adds the need to provision infrastructure for handling large datasets, distributed training, and model serving.
Monitoring and observability
DevOps and MLOps rely on monitoring systems to detect issues as early as possible and ensure reliability. Tools like Prometheus and Grafana are commonly used in both areas.
Both disciplines need to monitor application performance, uptime, and logs for debugging. However, MLOPs add monitoring for model-specific metrics, such as accuracy, drift, and latency.
When to Use MLOps or DevOps
Now that we’ve discussed the areas of overlap and where these disciplines differ, we can discuss when to use which discipline.
As discussed earlier, MLOps is data-centric and always makes sense when you have data-centric projects. Such projects could involve autonomous driving, real-time analytics, or image recognition.
However, MLOps is also essential for products where machine learning is a core feature, such as recommendation systems (e.g., Netflix, Amazon), fraud detection in financial services, or natural language processing applications like chatbots. You can also use MLOps whenever an ML model needs to be updated and retrained frequently.
DevOps, in contrast, makes the most sense to use in traditional software applications that focus on web services, mobile apps, or APIs, to name a few examples. These could include e-commerce platforms, content management systems, or Software-as-a-Service (SaaS) tools.
DevOps also makes sense whenever you aim to shorten the development cycles and deploy new features quickly while ensuring system reliability.
Challenges and Considerations
MLOps and DevOps are powerful disciplines that simplify workflows and improve efficiency. However, they also come with their challenges.
Cultural challenges, toolchain complexities, and scalability issues must be addressed to implement these disciplines successfully.
I’ll focus on a few challenges I’ve experienced in the following subchapters.
Cultural challenges
DevOps involves collaboration between software developers and operations teams.
MLOps adds data scientists, data engineers, and ML engineers. This increases the need for cross-functional collaboration.
Each of these groups has their priorities and expertise:
- Software developers: Focus on code.
- Operations teams: Focus on infrastructure and its reliability and scalability.
- Data scientists: Prioritize experimentation and model performance, so it's code but less focused on clean code principles, like a software developer.
- Data engineers: Focus on data pipelines.
- ML engineers: Focus on infrastructure and scalability for ML applications.
These different expertise and priorities can often lead to communication issues and differing goals.
Change resistance is another challenge to overcome. Both disciplines are disruptive. They aim to automate workflows and change the way people think and work. This often leads to people resisting and refusing to implement these workflows into their day-to-day lives.
Toolchain complexity
Both disciplines come with various tools that need to be provisioned and learned. However, the tool landscape for DevOps is less complex and more standardized than for MLOps.
MLOps involves a variety of tools, and their integration can be complex and time-consuming.
Teams must invest time in learning these tools, while operations teams must integrate them into their company's infrastructure, which can lead to difficulties.
Scalability
Both disciplines also have their challenges with scalability. DevOps mainly focuses on scaling applications and services to meet user demand, while MLOps must address data and model growth.
For MLOps, more unique challenges arise. ML often requires processing large datasets and training large ML models, which can require unique infrastructure like GPUs or TPUs.
Data growth introduces challenges in data storage, processing, and other topics like versioning. In addition, models need to be deployed, and low-latency requirements can make model serving unique and challenging.
These challenges make it even more apparent why automation and IaC are key to dealing with these difficulties.
Conclusion
In this article, we’ve discussed MLOps and DevOps and their similarities and differences.
Both disciplines are essential to simplify workflows and improve efficiency.
However, DevOps mainly focuses on traditional software development and deployment processes, while MLOps focuses on data-centric and ML-centric projects.
MLOps itself builds upon the principles of DevOps, as ML projects also have software in them and, therefore, share the basics.
But MLOps extend them to address ML-specific challenges, like large and quickly changing datasets, ML models that need to be versioned and frequently retrained, and monitoring that needs to address issues like data drift and concept drift.
If you’re interested in learning more about MLOPs and DevOps, I recommend checking out the following courses:
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FAQs
What is the main difference between MLOps and DevOps?
MLOps manages the machine learning lifecycle, including data workflows, model training, and monitoring, while DevOps focuses on traditional software development and deployment processes.
Can MLOps and DevOps be used together?
Yes, MLOps builds on DevOps principles. Most machine learning applications also involve software development, so combining both practices is often required.
What are some standard tools used in MLOps and DevOps?
In MLOps, popular tools include MLflow, Kubeflow, and SageMaker, while DevOps commonly uses Jenkins, Kubernetes, and Terraform for CI/CD and infrastructure automation.
What skills are needed to implement MLOps effectively?
Implementing MLOps requires expertise in machine learning, data engineering, and DevOps principles, as well as familiarity with tools like Kubernetes, Airflow, and MLflow.
I am a Cloud Engineer with a strong Electrical Engineering, machine learning, and programming foundation. My career began in computer vision, focusing on image classification, before transitioning to MLOps and DataOps. I specialize in building MLOps platforms, supporting data scientists, and delivering Kubernetes-based solutions to streamline machine learning workflows.
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