MLOps vs. DevOps: What's the difference?

Machine Learning Operations (MLOps for short) is a key aspect of machine learning (ML) engineering focused on simplifying and accelerating the process of delivering ML models to production as well as maintaining and monitoring them. MLOps involves collaboration between different teams, including data scientists, DevOps engineers, IT experts, etc.

MLOps can help organizations create and improve the quality of their AI and machine learning solutions. Adopting MLOps allows machine learning engineers and data scientists to collaborate on improving model performance by implementing continuous integration and continuous deployment (CI/CD) practices. It accelerates the ML model development process by integrating proper monitoring, governance, and validation of ML models.

What is DevOps?

DevOps combines the concepts of development and operations and describes a collaborative approach to performing tasks typically associated with separate application development and IT operations teams. In the broadest sense, DevOps is a philosophy that encourages improved communication and collaboration between these (and other) teams within an organization.

In the narrowest sense, DevOps refers to the adoption of practices that enable the deployment and maintenance of iterative application development, automation, and programmable infrastructure. It also includes changes in workplace culture, such as trust building and connections between developers, sysadmins, and other team members. DevOps aligns technology with business goals and can transform the software delivery chain, job functions, services, tools, and best practices.

MLOps vs. DevOps: Key Differences

Here are some of the key differences between MLOps and traditional DevOps.

Development

The concept of development refers to different things in each model, and CI/CD pipelines are slightly different.

DevOps:

• Typically, code creates an interface or application.
• Wrap code into an executable or artifact before deploying and testing with a set of checks.
• Ideally, this automated cycle would continue until the final product is ready.

MLOps:

• This code enables teams to build or train machine learning models.
• Output artifacts include serialized files that can receive data input to generate inferences.
• Validation involves checking the performance of the trained model against test data.
• This cycle should also continue until the model reaches the specified performance threshold.

Version Control

DevOps:

• Version control usually only tracks code and artifacts changes.
• There are very few metrics to track.

MLOps:

• MLOps pipelines typically have more factors to track. Building and training ML models involves an iterative experiment cycle that requires tracking various metrics and components for each experiment (critical for later auditing).
• Other components to track include training datasets, model building code, and model artifacts.
• Metrics include hyperparameters and model performance metrics, such as error rate.

Reusability

DevOps:

• DevOps Pipelines focus on repeatable processes.
• Teams can mix and match processes without following a specific workflow.

MLOps:

• MLOps pipeline applies the same workflow repeatedly. A common framework across projects helps improve consistency and allows teams to make progress faster because they start with a familiar process.
• Project templates provide structure and enable customization to meet the unique needs of each use case.
• Use centralized data management to consolidate your organization’s data to accelerate the discovery and training process. Common approaches to centralization include single sources of truth and data warehouses.

Continuous Monitoring

Monitoring is essential for both DevOps and MLOps, but for slightly different reasons.

DevOps:

• Site Reliability Engineering (SRE) has been a trend in the past few years, emphasizing everything from development to The necessity of production deployment monitoring software.
• Software does not degrade like ML models.

MLOps:

• Machine learning models degrade rapidly and require continuous monitoring and updates.
• Conditions in the production environment can affect the accuracy of the model. After being deployed to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance.
• MLOps ensures algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.

Infrastructure

DevOps and MLOps both rely heavily on cloud technology but have different operational requirements.

DevOps relies on infrastructure, such as:

• Infrastructure as Code (IaC)
• Build Server
• CI/CD Automation Tools

MLOps relies on the following infrastructure:

• Deep Learning and machine learning frameworks
• Cloud storage for large datasets
• GPUs for deep learning and compute-intensive ML models

DevOps and MLOps Trends

Here are some of the major trends driving DevOps and MLOps.

GitOps

As a new evolution of DevOps workflows, GitOps is a new paradigm for controlling and automating infrastructure. Paradigms for Kubernetes enable developers and operations teams to use Git to manage Kubernetes clusters and deliver containerized applications. Implementing Git workflows for operations and development teams allows developers to leverage Git pull requests to manage software deployments and infrastructure.

GitOps integrates existing development tools to manage cloud-native and cluster-based applications through CI/CD. It automatically deploys, monitors and maintains cloud-native applications using Git repositories as a single source of truth.

GitOps is a way to implement and maintain clusters in Kubernetes. Continuous delivery and deployment allow developers to build, test, and deploy software faster through incremental releases. Kubernetes continuous integration and runtime pipelines must be able to read and write files, update container repositories, and load containers from Git. GitOps helps enterprises manage their infrastructure through version control, real-time monitoring, and configuration change alerts.

Synthetic Data

Synthetic data is any information that is artificially generated rather than gathered from real events. The algorithm generates synthetic data that is used as a surrogate for operational and production test data sets. Synthetic datasets can also be used to validate mathematical models and train machine learning models.

Benefits of synthetic data include:

• Minimize restrictions associated with the use of sensitive and regulated data.
• Customize data based on specific requirements and conditions not available in real data.
• Generate data for DevOps teams to test software quality and performance.

Codeless Machine Learning and Artificial Intelligence

Machine learning often involves computer code to set up and handle model training, but this is not always the case. Codeless machine learning is a programming approach that eliminates the need for ML applications to go through time-consuming processes.

CodelessML eliminates the need for experts to develop system software. It is also simpler and cheaper to deploy and implement. Using drag-and-drop input during machine learning simplifies training by:

• Evaluating the results.
• Drag and drop training data.
• Create forecast reports.
• Use plain text query.

#Codeless ML makes machine learning applications easily accessible to developers, but it is not a substitute for advanced, nuanced projects. This approach is suitable for small businesses that lack the funds to maintain an in-house data science team.

TinyML

TinyML is a new approach to machine learning and artificial intelligence model development. It involves running models on devices with hardware constraints, such as the microcontrollers that power smart cars, refrigerators, and electricity meters. This strategy works best for these use cases because it speeds up the algorithm - data does not need to be transferred back and forth on the server. It is especially important on large servers and can speed up the entire ML development process.

There are many benefits to running TinyML programs on IoT edge devices:

• Lower energy consumption.
• Reduce latency.
• User privacy guaranteed.
• Reduce bandwidth requirements.

Using TinyML provides greater privacy because the calculation process is completely local. It consumes less power and bandwidth, resulting in lower latency because it does not require data to be sent to a central location for processing. Industries that are taking advantage of this innovation include agriculture and healthcare. They typically use IoT devices embedded with TinyML algorithms to use the collected data to monitor and predict real-world events.

Conclusion

This article introduces the main differences between MLOps and DevOps:

##Development - DevOps Pipelines focus on developing new versions of software products, while MLOps focus on delivering effective machine learning models.
• Version Control – DevOps focuses primarily on tracking binaries and software artifacts, while MLOps tracks other factors such as hyperparameters and model performance.
• Reusability – DevOps and MLOps both strive to create reusable processes and pipelines, but use different strategies to achieve reusability.
• Continuous monitoring - Monitoring is important in DevOps, but even more important in MLOps because model and data drift can cause model performance to degrade.

Finally, we introduce some key trends that will change DevOps and MLOps in the near future. I hope this will help you discover your place in the new and exciting development ecosystem.

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