It’s the age of automation, and it’s not just in manufacturing. Nowadays, systems of all kinds are being automated to reduce human error and increase efficiency. Systems like DevOps, ModelOps, and MLOps are all driven by data. So what is the difference between them?
Before comparing MLOps, DevOps, and ModelOps, it's important to first understand what MLOps, DevOps, and ModelOps are, how they work, and what benefits they provide.
This article will go through their life cycles, concepts, best tools, and platforms. You can skip directly to the part you're interested in.
What is MLOps?
MLOps is a business model that machine learning organizations have devised. The concept of MLOps replaces the traditional vertical silos in which organizations function by sharing resources and expertise across departments. MLOps is a way for data scientists and operations experts to collaborate and communicate in order to manage the production ML lifecycle. It is a culture and practice in machine learning engineering that tries to bring together the creation and operation of machine learning systems (Ops).
MLOps Principles
MLOps is relatively new, as machine learning has only recently become broadly adopted by companies. MLOps principles are intended to change the game in various sectors. So, let's look at some of the MLOps' core components or principles.
- Automation - You can easily automate the workflow steps without any manual intervention.
- CI/CD - MLOps includes CI/CD. Testing and Monitoring.
- Versioning - You can efficiently track your ML Models and datasets with version control systems.
- Experiment Tracking - You can parallely execute multiple experiments on model training.
- Testing - Test different features, data, models, infrastructure, and a lot more.
- Monitoring - Get detailed insights on models performance.
Recommended Reading: Implementing MLOps
Why MLOps?
ML is different from standard software because data is at the core of the application. This means that the code is built around servicing the data rather than the application behavior. ML is also an open-loop, ever-evolving system. The task has barely just begun after the models have been deployed. To achieve maximum performance, models in production must be constantly monitored, retrained, and redeployed in response to changing data signals. MLOps drive insights you can trust and put into play more quickly in a controlled manner. It brings back business interest into machine learning operations.
- It combines the expertise of all teams for more efficient ML
- It inculcates best practices & guidelines to ensure a smooth transition from concepts to experiments to production-ready models.
- It helps avoid bottlenecks with a better division of expertise.
- You can quickly train your models, track experiments, and deploy strong APIs.
How MLOps Works?
Generally, MLOps lifecycle/workflow involves the following steps-
- Data Extraction
- Data Analysis
- Data Preparation
- Model Training
- Model Evaluation
- Model Validation
- Serving & Monitoring
MLOps can be implemented in three different ways:
Level 1: Manual process
This process is common for businesses just getting started with machine learning. If your models are infrequently altered or trained, a manual ML approach could work.
This is a standard data science technique used at the beginning of machine learning deployment. This level is experimental and iterative in nature. Each pipeline step, including data preparation and validation, model training, and testing, is done manually. Data is frequently handled using Rapid Application Development (RAD) methods like Jupyter Notebooks. When employing the manual process, the probability of model failure in the real world rises.
Recommended Reading: What is MLOps — Everything You Must Know to Get Started
Level 2: ML Pipeline Automation
This architecture is ideal for deploying new models based on fresh data rather than new machine learning ideas.. It automates the ML pipeline, leading to faster experimentation. It also allows you to achieve CD of model prediction service and automate the process of retraining models in production with new data. You must arrange the ML experimentation processes, introducing things like automatic data, model validation, and metadata management, as seen in the graphic below.
To develop ML pipelines, components must be reusable, compostable, and shareable across the pipeline. As a result, EDA code may be stored in notebooks, but component source code must be modularized. This scenario might be useful for systems that operate in a dynamic environment and need to quickly react to changes in customer behavior, price rates, and other factors. However, if you want to experiment with new machine learning concepts and quickly deploy new ML implementations, you'll need a CI/CD infrastructure to automate the build, test, and deployment of your ML pipelines.
Recommended Reading: How to scale machine learning models?
Level 3: CI/CD Pipeline Automation
This level is appropriate for tech-driven businesses that must retrain their models daily, if not hourly, update them in minutes, and redeploy them across thousands of servers simultaneously. Such enterprises will not be able to thrive without an end-to-end MLOps cycle. The Data, Machine Learning Model, and Machine Learning Training Pipeline components are now created, tested, and deployed automatically, which is a significant improvement over the previous level.
This method allows data scientists to rapidly explore new ideas around feature engineering, model design, and hyperparameters. In the automated level pipeline, you can see the six primary phases of CI/CD, as shown in the graphic below. With this, you can repeatedly test new machine learning algorithms and modeling while orchestrating the experiment phases. The source code for the ML pipeline stages is output from this stage, subsequently published to a source repository.
Advantages Of MLOps
Many businesses now use machine learning operations to optimize their production and distribution systems, as well as further research and development efforts, such as those in self-driving cars. MLOps allow companies to drastically decrease the time needed for data analysis by creating automated feedback loops that can find patterns within a massive dataset without human aid. Here are a few benefits of MLOps:
- Rapid Innovation.
- Reproducible Workflow and models.
- High-precision models can be easily deployed in any situation.
- Effective management of the entire ML lifecycle
- A resource management system and control for ML models.
Best MLOps Tools & Platform
There are several end-to-end MLOps and custom-built MLOps tools available; let's have a look at a couple of the best and most widely used platforms.
End-to-end MLOps solution
These are completely managed services for rapidly creating, training, and deploying machine learning models.
Custom-built MLOps platform
End-to-end solutions are excellent, but by separating your MLOps pipeline into numerous microservices, you can create your own MLOps tool stack with your chosen tools. These platforms are best for businesses just getting started with machine learning.
A variety of Open Source frameworks have arisen in the few short years that MLOps has gained prominence. As data and technology continue to proliferate and reach new heights, implementing solid ML strategies today will help enterprises of all types manage and grow in the future.
Learn how to improve model health with Censius AI Observability Platform
What is DevOps?
Any software company consists of two teams: one is the development team, which designs and develops systems from scratch, and the other is the operations team, which tests and implements the developed products. In most cases, the operations team provides input on bugs and other issues while the development team handles them. There may be times when the development team moves on to a new project while the operations team provides input on the prior one. This pushed back the deadline and delayed the entire software development cycle. T
What if both the development and operations teams collaborated and the barrier of misunderstanding was broken? This is what the DevOps concept is all about. DevOps has become a popular term used in the IT world to describe the process of software development, operation, and management across a system. It is not just one tool or method which can get the job done; instead, it is an approach that allows for greater agility when it comes to changes in operations. DevOps is primarily about collaboration between developers and manual and automated systems.
How DevOps Works?
Key Components of DevOps lifecycle involves :
- Source Code Management : Version control and maintaining different versions of code
- Continuous Integration: Continuous build, compile, validate, code review & unit testing
- Continuous Delivery: Continuous testing and deploying the build applications to test server
- Continuous Deployment: Configuration management & containerization, deploying application to prod server for release
- Continuous Monitoring: monitoring every part of lifecycle
We will look at each stage briefly, so let's start with source code management.
Source Code Management/Version Control
The practice of tracking changes to a source code repository is referred to as source code management (SCM). SCM keeps track of a code base's history of modifications and assists in resolving disputes when merging updates from various contributors. Version control is also referred to as SCM. There are two types of version control systems:
Centralized Version Control
Everyone commits and edits the code simultaneously in a single repository with centralized version control. Everyone on the team will be notified when the code is updated, and they will be required to update the code in their workspace.
Distributed Version Control
With distributed version control, each developer has their own repository and software copy. You have to submit the updated code to the central repository. To put it another way, you commit and push the changes while other team members pull and update them.
Recommended Reading: Source code management best practices
Continuous Integration
Continuous Integration (CI) is a technique for integrating feature branches into master code that can be automatically generated and tested. This strategy assists developers in discovering and quickly fixing problems. You can fail early and improve rapidly with CI integration, which improves the quality of your process. CI helps create a pipeline from coding an idea to getting it out into the world.
Recommended Reading: Best Data Version Control Tools
Continuous Delivery
Following Continuous Integration (CI), you have to test applications for end-user acceptability in the Continuous Delivery stage. Continuous Delivery is a software development approach in which teams generate software in short cycles to be deployed to end-users at any time. The team manages risk by ensuring that each increment is small and contains changes that are only related to one aspect of the product—so if something goes wrong, it won't affect other aspects of the development.
Continuous Deployment
Continuous Deployment is taking the test application and deploying it to the production server— every change is deployed to production. It's the opposite of a big bang release. This is done by automating your deployments, creating small deployments, and using feature flags to control which users get what features.
Recommended Reading: What's the difference between CI and CD
Configuration Management
Configuration management (CM) is the process of tracking and ensuring that all instances of a particular product, application, or resource are changed in unison. It is an important part of any infrastructure because it ensures consistency among infrastructure elements. It eliminates dependency issues.
Continuous Monitoring
Continuous monitoring refers to an organization's capacity to identify, respond to, contain, and mitigate attacks on its infrastructure. It is the process of collecting and analyzing data over time, in order to detect and prevent problems that may not be observable in real-time. Continuous monitoring helps business owners and managers sense changing conditions and trends quicker than traditional approaches.
Advantages Of DevOps
The whole idea behind DevOps is to create a fast-paced environment where software can get from concept to production quickly without sacrificing quality or performance. This not only increases customer satisfaction but also helps the company grow revenue while. DevOps helps you :
- Ensure a more rapid deployment
- Maintain a stable working environment
- Significantly higher product quality
- Software delivery on a continuous basis
- Collaboration and communication improvements
Best DevOps Framework, Tools and Platforms
Here is a list of some of the most popular DevOps tools and frameworks.
Operate: Chef, Kubernetes
What is ModelOps?
Every organization's enterprise AI strategy revolves around ModelOps. ModelOps is a system that allows multiple AI objects, solutions, and frameworks to be converged while retaining scalability and governance.
In simple terms, ModelOps is an MLOps extension. MLOps should be built up first in an organization that wants to use ModelOps. ModelOps requires the same talents as MLOps, plus a few additional skills relating to IT operations, risk management, governance, and a few more.
Why ModelOps?
Industry, technology and data must all work together to scale AI in business trends. The organizational data must be opened to travel freely across the company. This cannot happen in a compartmentalized work environment; therefore companies must form an interdisciplinary team to drive AI forward. ModelOps is used by businesses to address issues such as:
Model Number - To account for variances in business processes, customization, and specialized customer groups, each organization will need to manage hundreds of models.
Complexity - Even the most experienced IT teams are overwhelmed by the unmanageable complexity of data and analytics innovation.
Regulatory Compliance - As the usage of artificial intelligence increases throughout markets, complying with tight and ever-increasing models becomes more difficult.
Silos Environment - Multiple teams are involved in a model as it progresses from deployment to monitoring. Scaling AI might be problematic due to ineffective team coordination. ModelOps helps create an environment where models can easily be moved from the data science team to the IT production team.
Recommended Reading: ModelOps use case
How to put ModelOps into practice?
ModelOps tasks are quite similar to MLOps tasks. Enterprises must monitor the ModelOps software's performance as the first stage in the ModelOps process. You must track each model's progress, set accuracy targets for models (or model classes) and track them for dimensions like drift and deterioration during development, validation, and deployment. Determine which market indicators are impacted by the model in service. Keep track of how much space processing power or memory models take up. Generally, ModelOps includes working on:
- CI/CD
- Development environments
- Testing
- Model versioning
- Model store
Advantages of ModelOps
Major corporations have used ModelOps to accelerate their artificial intelligence and machine learning applications for the past few years. Machine learning models must be run throughout an organization to adapt to hundreds of use cases.
- ModelOps is the connecting thread between all of the other elements of the AI pipeline.
- Reduce the risks of "hidden" solutions developed outside of the IT department's supervision.
- Reduce inconsistencies, resulting in improved resource allocation and higher model reuse.
Best ModelOps Tools and Platforms
Here are some model-driven platforms:
The Comparison: MLOps vs. DevOps vs. ModelOps
MLOps refers to the procedures involved in operationalizing and managing AI models in production systems, while ModelOps is considered a superset of MLOps. ModelOps has an advantage over MLOps in that MLOps focuses just on machine learning models, whereas ModelOps aims to operationalize all AI models. Although the concepts of these three terms might be similar, they are not the same.
Let's look at the differences between DevOps, MLOps, and ModelOps.
What Is It?
- MLOps is a data science process that involves rapid testing and deployment of machine learning models.
- DevOps is a method that combines both development and IT operations working together to make things more efficient, reliable, and secure.
- ModelOps is enterprise governance and operations for models in production.
Primary User
- MLOps is generally used by data scientists & MLOps engineers.
- DevOps is used by software developers, DevOps Engineers.
- ModelOps is used by the IT or Business Operations team.
Key Capabilities
MLOps
- It helps avoid bottlenecks with a better division of expertise.
- Easy integration with data science platforms.
- During model deployment, models are quickly deployed for experimentation and testing.
DevOps
- Ensure a more rapid deployment
- Significantly higher product quality
- Software delivery on a continuous basis
ModelOps
- Complete model lifecycle automation.
- Inventory of product models across the entire organization.
Let's compare a few technical aspects. Because ModelOps is an extension of MLOps, certain aspects may be similar. We will see MLOps vs. DevOps in most technical aspects.
Versioning
- In a DevOps pipeline, version control is often limited to tracking changes to code and artifacts. There are more things to track in an MLOps pipeline.
- MLOps is an experimental field in which you have to test various techniques to discover the optimal solution to the problem rapidly. Each experimental run's components and metrics must be tracked to appropriately re-create it for auditing reasons later on.
Testing
- Testing a machine learning application is more complicated than testing other types of software.
- You'll also require data validation, trained model quality assessment, and model validation in addition to standard unit and integration tests.
Deployment
- On the conventional DevOps side, you'll usually have code that generates some form of application or interface. The code is subsequently packaged into an executable (artifact), which is then deployed and tested against a set of criteria. This procedure should ideally be automated and continued until a finished product is obtained.
- In MLOps, on the other hand, the code is used to create/train a machine learning model. The output artifact, in this case, is a serialized file that may be fed data to generate conclusions. Validation would entail comparing the training model's performance against test data. Similarly, this cycle repeats until the model achieves a specified level of performance.
Monitoring
- Once a model is deployed into production, it begins to generate predictions from new data that it receives from the real world. Model drift is an extra component to monitor in MLOps, in addition to the application itself, because data is continuously changing.
- ModelOps focuses on model governance and comprehensive lifecycle management, where you have to ensure that predicted business value, operational efficiency, and risk and compliance mandates are satisfied.
Real World Use Case
Banks, for example, have long used statistical models to approve credit, and real-time analytics now drive the majority of operational decision-making. This model-based approach has assisted banks in reducing man-hours, but scaling these complex models is challenging. These models should be fair and reliable, allowing for unbiased decision-making. ModelOps makes it easy to check for bias or anomalies in the model and change it as needed.
There's a reason why companies like Facebook have been using infrastructure as code and DevOps practices, but MLOps go beyond those methods by operating at a higher level. They allow the company to measure and track the performance of each employee to see who is performing best and which ones will be most effective in achieving the company's goals. Let's summarize everything we have learned so far.
Other Terms
What isn’t MLOps
Because MLOps is a term that gets tossed around a lot, it's important to know what it isn't first:
- MLOps should not be confused with DataOps, an area of data science that focuses primarily on data pipelines, providing valuable insights by connecting disparate data sources and having flexible data workflows at scale.
- MLOps is not the same as AIOps, as process automation takes place within an organization's IT operations department in AIOps rather than the machine learning team.
- MLOps is not your typical software, which is developed independently and then linked to the production model after it is transformed. The nature of MLOps is far more exploratory.
- MLOps should not be confused with ModelOps, which is an area of Modeling that focuses on the deployment of trained models in production systems. ModelOps is the discipline of developing and deploying models into production systems.
DataOps
DataOps is the practice of taking data system operations and moving them into an automated, repeatable, scalable environment. It is a software engineering discipline that emphasizes managing data with high-performance analytics.
AIOps
AIOps stands for Artificial Intelligence Operations. AIOps uses AI to analyze data and then optimize operations with that data. It is used to automate processes or identify patterns that humans would not have seen.
Conclusion
The days of a single project team maintaining a system from end to end are gone. This is because of two major reasons:
- Businesses have become more complex, needing to do more with less time and resources.
- IT systems have been changing from monolithic to a micro services architecture
Throughout this article we've studied what MLOps, DevOps and ModelOps are and how businesses can use them to operationalize AI solutions. We discussed several platforms and technologies that may assist in your software/ML workflow, monitoring, and governance. We also discovered how MLOps and ModelOps are distinct but related concepts.
I hope you liked this in-depth guide about MLOps, DevOps, and ModelOps. Happy Experimenting!
PS: Censius AI Observability allows you to gain deeper insights into your machine learning pipelines! With MLOps, DevOps, and ModelOps becoming increasingly complex, it's more important than ever to have visibility into your models and infrastructure. Our tool provides real-time monitoring and visualization of your ML pipelines, allowing you to quickly identify and troubleshoot any issues that arise.
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References and Recommended Reading
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