Content Map
What Is MLOps?
What Is DevOps?
Similarities between MLOps and DevOps
Key Differences between MLOps and DevOps
Which One Should You Choose: MLOps or DevOps?
Final Thoughts
Ever come across the term DevOps? Well, it has been in circulation for quite some time, referring to the collaboration between software developers and IT operations personnel for enhanced software development. But recently, a new concept has emerged on the scene – MLOps. This innovative approach combines the principles of DevOps with machine learning (ML), gaining traction as companies strive to accelerate machine learning project deployment.
At first glance, you can see the clear similarity – they both emphasize collaboration among developers, operations teams, and data scientists; and the obvious difference – while DevOps leans towards application development, MLOps focuses on machine learning. However, there are many more differences between these two concepts, which we will uncover in just a few seconds.
So, without further ado, let’s break down MLOps vs DevOps – one term at a time!
MLOps is a new way of doing business for machine learning teams. Instead of working separately in different company parts, they all team up and share their expertise. It thus allows data experts and operations professionals to work well together, managing the whole machine learning production lifecycle.
Plus, MLOps can also be seen as a culture and practice that unites the making and running of machine learning systems.
Typically, the MLOps lifecycle or workflow includes the following stages:
Meanwhile, MLOps implementation can be done in three different ways:
Numerous businesses have leveraged machine learning operations to enhance their production and distribution systems, as well as to advance research and development. It enables companies to significantly reduce the time required for data analysis by establishing feedback loops that can identify patterns within extensive datasets without human assistance. There are many more benefits of MLOps:
Although various comprehensive and tailor-made MLOps tools are available, picking the right one has never been easy. Below are a few common solutions that you should know:
End-to-end MLOps solution
These are fully managed services designed for the rapid development, training, and deployment of machine learning models.
Custom-built MLOps platform
While end-to-end solutions are great, breaking down your MLOps pipeline into different microservices allows you to build a personalized MLOps toolset using your preferred platforms. These are particularly beneficial for companies just embarking on their machine learning journey:
There are often two teams in a software company: development and operations. While development teams are responsible for building new systems, the operations teams test and implement those products. However, there can be delays in the development cycle because the azz operations team sometimes finds bugs in products.
That’s when DevOps comes in handy. This concept promotes the collaboration between development and operations teams, thereby striving to build and deliver software as quickly as possible.
Key principles of the DevOps lifecycle include:
Now, take a quick look at each stage!
1. Source Code Management/ Version Control
Source code management (SCM), aka. version control is the practice of tracking changes made to a code repository. SCM maintains a history of the code base’s modifications and helps resolve issues when combining updates from other contributors.
There are two kinds of version control systems:
In this system, users work on the code together in one repository simultaneously. Team members receive notifications when the code is updated and will have to sync their code workspace in response.
In contrast, distributed version control provides each developer with their own repository and software copy. You need to submit code changes to the central repository so your team members can pull and update them accordingly.
2. Continuous Integration
Continuous integration (CI) is a method of automatically integrating feature branches into the master code, which helps build a pipeline from coding an idea to putting it out to the world. This enables developers to identify and fix problems quickly, resulting in rapid improvement and enhanced quality of the software development process.
3. Continuous Delivery
After CI, applications must undergo testing for end-user acceptability in the Continuous Delivery (CD) stage.
CD is a software development approach where teams produce software in short cycles, ready for deployment to end-users at any time. By keeping each increment small and focused on specific changes, your team can mitigate risks, preventing one issue from affecting the entire development process.
4. Continuous Deployment
Continuous Deployment means taking the tested application and deploying it directly to the production server, with every change made visible. Through this stage, developers can release new features and bug fixes to customers at a quicker pace, reducing the time taken from ideation to execution dramatically.
5. Continuous Monitoring
Continuous monitoring refers to a company’s ability to detect, react, contain, and prevent attacks on its infrastructure. It also involves gathering and evaluating data over time to identify and avoid potential issues that might not be obvious in real time. Managers and business owners can thus see patterns and changing situations more quickly than when adopting conventional methods.
DevOps functions through a 4-stage lifecycle, forming an infinity loop. These phases establish a continuous feedback loop, letting businesses quickly bring thoroughly tested solutions to customers.
1. Design and Plan
The first stage of the DevOps lifecycle is planning. Here, stakeholders from the business, development, and IT teams work together to decide which features to include in the next development cycle and evaluate their business value.
2. Continuous Integration
The following is continuous integration, a 3-step DevOps strategy that includes coding, building, and testing. The use of automation in this step seeks to boost the final product quality.
In this model, the central repository is regularly updated with code changes, and then an automated tool will retrieve the code base and evaluate its quality. After that, a binary solution is produced and functionally tested, manually or automated, to give the organization comprehensive reports.
3. Continuous Deployment
The CD phase integrates the Release and Deploy stages. Any build that has undergone quality testing will automatically be released and deployed to production or pre-production environments. Moreover, the DevOps team can change the frequency of releases and deployments as necessary.
4. Continuous Monitoring
The last continuous monitoring phase covers two parts: Operate and Monitor. It entails supervising software operations, acquiring essential security and health data, and engaging with team members to quickly and effectively address any issues.
At its core, DevOps aims to establish a fast-paced environment in which software progresses rapidly from conception to production without negatively diminishing quality or performance. This not only enhances customer satisfaction but also contributes to revenue growth.
DevOps helps you:
Some of the most common DevOps tools and frameworks are listed below:
Since MLOps is a subset of DevOps, there are several key similarities between the two concepts:
Despite certain similarities, DevOps and MLOps differ in their focus and the unique challenges they face in their respective fields. While MLOps is dedicated to the specific needs of machine learning operations, DevOps is intended to streamline software development and delivery processes.
But there are more differences than just that.
Criteria | MLOps | DevOps |
Core Objective | Attempt to enhance the model deployment and management of machine learning models. | Aims to accelerate software delivery and dependability. |
Core Activities | Model training, Validation, and Monitoring. | Code integration, Testing, and Deployment. |
Primary User | Data scientists, machine learning engineers, analysts, and IT Ops. | Software engineers and DevOps engineers. |
Key Capabilities | - A greater division of specialization helps minimize bottlenecks. - Integration with data science platforms gets easier. - Models are promptly deployed for experimentation and testing during model deployment. | - Ensure a faster deployment - Considerably higher quality product - Software delivery on an ongoing basis |
Main Components | Data pipelines, model registries, and monitoring | Code repositories, CI/CD pipelines, and infrastructure. |
Data Handling | ML-specific data and models | Code and application-related data |
Key Challenges | Model drift, Data bias, Model explainability | Continuous integration, Infrastructure management |
Key Tools | Kubeflow, MLflow, TensorFlow Extended (TFX) | Jenkins, GitLab, Travis CI, Docker, Kubernetes |
Versioning | Need to quickly find the best solution to the problem by testing various approaches. To properly re-create an experimental run for auditing purposes, the metrics and components of each run must be tracked later. | It tends to be confined to documenting changes to code and artifacts, whereas in an MLOps pipeline, there are additional things to keep track of. |
Testing | Encompass model validation, model training tests, etc. This causes a lengthened MLOps testing period. | Continuous testing in DevOps is relatively quick since it only uses unit and integration testing to validate the workflow, quality, and business risks related to the product. |
Deployment | Use the code to build and train a machine learning model. The output artifact is a serialized file that can be used to feed data and derive conclusions. To gain validation, you need to compare the performance of the training model to that of test data. This procedure should continue until the model gets the desired level of performance. | Have code that creates some application or interface. The code is then packed into an executable (artifact), deployed, and evaluated against different criteria. This process should ideally be automated and repeated until a completed product is achieved. |
Monitoring | MLOps projects witness continuous data transformation. Due to this constant change, the ML model must be updated and adjusted frequently. Therefore, model drift is another factor to monitor in MLOps and the application. This prolongs the production time, especially if the data shows seasonality. | Although DevOps’ ultimate goal is to provide services and apps quickly, its entire lifecycle is constrained by the tight integration of model development, testing, and operation. |
To choose between MLOps and DevOps, you must first determine your own needs and objectives. If your company wants to create and implement machine learning models, then MLOps is a better option. However, you should go for DevOps if your company aims towards overall software development.
On the one hand, with MLOps, you can effectively deal with primary challenges involved in building, maintaining, and deploying machine learning models, such as data management, model versioning, and production performance monitoring. On the other hand, DevOps can assist you in enhancing teamwork and communication among teams inside your business to boost the overall pace and quality of your software development and deployment.
Another way to make a decision is to base it on how mature your company is. DevOps is a more general approach, so if ML development is still in its early stages, it is preferable to start there. Then, as ML development progresses, MLOps may be added gradually.
Knowing the ins and outs of MLOps vs. DevOps is a game-changer for companies wanting to adopt either approach for their automation game. The key takeaway from this detailed comparison piece is that MLOps dives into the intricacies of machine learning models, making workflows smoother, models deploy faster, and management a breeze, while DevOps aims towards optimized software development, ensuring speedy product delivery and seamless collaboration between development and operations teams.
Also, remember, a data-driven powerhouse can’t thrive without embracing DevOps principles. And if your business involves dynamic data and supercharged machine learning models for top-notch prediction accuracy, MLOps principles should take center stage.
Deploying an ML model requires not only machine learning engineers and data scientists but also a clear approach to validate a solution that suits your demands. The Neurond MLOps consulting service provides an end-to-end solution, from ML strategy development to product launch.
Now, ready to elevate your automation game? Contact us now for your next successful MLOps development!
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