With artificial intelligence and machine learning technologies becoming an integral part of most business operations, MLOps vs DevOps has become a point of debate in the software development space in recent times, such as DevOps and MLOps tools.
DevOps, a set of practices that aims at breaking silos between development and operations teams, helps organizations implement seamless collaboration, automation, and continuous delivery, which brings faster and more reliable software development and deployment systems onto the table.
Machine Learning Operations (MLOps) extends DevOps functionality to address specific challenges associated with deploying and managing machine learning models. Right from data preparation and model training to deployment and maintenance of models, MLOps streamlines the end-to-end ML lifecycle. This blog toms at decode MLOps vs DevOps.
- Understanding MLOps
- Top MLOps Tools
- Understanding DevOps
- Top DevOps Tools
- Bridging the Gap: MLOps vs DevOps Similarities
- Differences Between MLOps vs DevOps
- MLOps vs DevOps Comparison Table
- Conclusion
- FAQs
Understanding MLOps
How MLOps Works?
Before entering into the MLOps vs DevOps debate, it is important to understand how each concept works. MLOPs integrate machine learning workflows into the DevOps framework while adapting its practices and principles unique to ML development. This approach incorporates operational efficiency into machine learning development.
Here is a typical ML workflow:
- Data Preparation: The MLOps workflow begins with acquiring data for ML models and cleaning and preparing that data for use. With proper data versioning and management, you can ensure reproducibility.
- Model Development: The next step is developing, training, and validating ML models. ML engineers and data scientists take up this job. ML artifacts such as models and feature engineering code are version-controlled as well.
- Model Deployment: Once an ML model is trained and validated, it should be deployed to a production environment. It is good to automate the deployment process for reliability and consistency.
- Monitoring and Logging: Deployed ML models are continuously monitored for performance degradation, concept drift, and other issues using MLOps tools. Logging provides insights into the model performance and behavior.
- Feedback Loop: A feedback loop is created to incorporate improvements to the Ml model based on the monitoring and user feedback. This is important to maintain the relevance of the ML model over time.
- Scaling and Retraining: Based on evolving requirements, data engineers scale and periodically retrain ML models to ensure their effectiveness.
In the MLOps vs DevOps context, both share common principles and practices in the form of CI/CD pipelines, automation, and feedback loops for the development, deployment, and monitoring of the end product.
Benefits of MLOps
In the MLOps vs DevOps debate, let’s review how organizations engaged in machine learning development can reap amazing benefits from MLOps. Here are a few of them:
- Faster Time-to-Market: By streamlining ML development pipelines and reducing bottlenecks, MLOps accelerates the deployment of models into production and thereby delivers faster time to market.
- Consistency and Reproducibility: By emphasizing version control for both code and data, organizations can ensure that ML experiments are reproducible and can be traced back to specific code versions and data sets. It ensures consistency and reproducibility.
- Improved Collaboration: MLOps breaks silos between the development, operations and data science teams to foster collaboration and a seamless flow of information and code.
- High Scalability: MLOps facilitates automated deployment and scaling mechanisms that allow organizations to deploy ML models at scale to address changing dynamic business requirements.
- Enhanced Monitoring and Maintenance: With continuous monitoring that detects issues early, MLOps enables proactive maintenance such that ML models remain effective and relevant over time.
- Reduced Costs: With automated and streamlined development pipelines, faster time-to-market and resource-efficient model deployment, and early detection of issues, MLOps contributes to overall savings.
In the MLOps vs DevOps context, most of these benefits also apply to DevOps environments.
Top MLOps Tools
Now, in the MLOps vs DevOps debate, here are the top 5 MLOps tools that are widely used to manage machine learning operations and ML model lifecycle.
AWS SageMaker
AWS Sagemaker is a fully managed MLOps service from AWS that covers the end-to-end machine learning lifecycle. It provides the right features for data labeling, model training, deployment, and monitoring, enabling developers to easily build, train, and deploy machine learning models at scale on AWS. Developers can interact with Sagemaker using multiple interfaces. The Web API enables developers to control Sagemaker server instances remotely. The provision of managed Jupyter Notebook instances allows developers to interactively program the tool and other apps.
Google Cloud Vertex AI
Vertex AI is a unified platform from Google Cloud that simplifies machine learning workflows. It comes with good features for data preparation, model training and deployment. A handy feature is the Automated machine learning (AutoML) capabilities that enable novice users without ML expertise use MLOps effectively.
Microsoft Azure ML Platform
Azure Machine Learning Platform offers an enterprise-grade end-to-end ML lifecycle management service with a comprehensive set of tools for building, training, and deploying ML models in Azure Cloud. It seamlessly integrates with Azure services and supports various languages and frameworks.
Databricks
Popular for its collaborative Apache Spark-based platform, Databricks provides a great platform for big data analytics and machine learning wherein data scientists, data engineers, and ML engineers can seamlessly collaborate on large-scale data processing and machine learning tasks.
MLflow
MLflow is an open-source platform that offers end-to-end machine learning lifecycle management. Being framework-agnostic, it allows developers to work with different ML libraries. It offers handy tools for experiment tracking, deploying models on various environments, and packaging code into reproducible runs.
Understanding DevOps
As you delve deep into MLOps vs DevOps, understanding how DevOps works is important, too.
How DevOps Works?
DevOps is a set of practices designed to improve seamless collaboration and communication between the development and operations teams throughout the software development lifecycle. The goal is to create shorter development cycles, deliver software faster, and enhance the quality and reliability of the delivered products.
Here are the key components of DevOps:
- Continuous Integration: Continuous integration ensures that developers are regularly integrating code changes and running automated tests to detect issues at an early stage.
- Continuous Deployment: Continuous deployment is about automating the release pipeline to rapidly deploy quality, secure and reliable code to production.
- Infrastructure as Code (IaC): It is a process of automating the provision and management of infrastructure using code. IaC ensures high scalability and consistency across the infrastructure.
- Continuous Monitoring: With feedback loops and continuous monitoring, organizations can ensure proactive issue resolution.
- Microservices Architecture: It is an approach to software development wherein a complex application is broken into smaller and independently deployable services that communicate through well-defined APIs. In addition to delivering scalability and agility, microservices architecture allows DevOps teams to deploy, manage, and update each service independently.
- Containerization: is a process of encapsulating an application along with all its dependencies into a lightweight, portable container to ensure consistent app performance across environments.
DevOps ensures a continuous improvement culture wherein you regularly assess and refine processes. DevOps culture emphasizes creating cross-functional teams comprising individuals with diverse skills and working with a holistic approach which means every member of the team is accountable throughout the software lifecycle.
Benefits of DevOps
DevOps transforms the software development lifecycle with seamless collaboration, automation, and customer-centric focus for enhanced business results. Here are a few key benefits of DevOps.
- Accelerated Delivery: DevOps streamlines the software development lifecycle by implementing continuous integration and continuous delivery (CI/CD) and thereby enabling developers to deliver quality code faster and better. This acceleration results in faster time-to-market while allowing organizations to quickly adapt to changing business requirements.
- Seamless Collaboration: By creating cross-functional teams, DevOps breaks silos between development, operations and other teams to incorporate a collaborative culture across the organization. While this approach enhances collaboration, it also creates a shared responsibility with better understanding and cooperation that in turn leads to more efficient and effective development processes.
- Increased Productivity: DevOps incorporates automation into the development lifecycle. With regular tasks such as build, test and deployment automated, teams get the luxury of focusing on creative aspects of the project. While it reduces human errors, it significantly increases the efficiency of processes and increases productivity.
- Reliable and Quality Code: With automated CI/CD pipelines and feedback loops, organizations can identify issues at the earliest stage and address them. It increases productivity while enhancing the quality and reliability of code.
- Reduced Costs: Organizations have to invest initially in the implementation of DevOps practices. However, the long-term benefits offer great cost savings. With automation incorporated across processes, human errors are eliminated and resources are optimally utilized—faster issue resolution results in lesser costs associated with fixing problems.
Whether you need a DevOps engineer or a complete team, ClickIT can help you automate your applications, decrease time to market, increase efficiency, and reduce IT costs. Book a call.
Top DevOps Tools
In the MLOps vs DevOps debate, let’s review the DevOps tools, also referred to as DevOps technologies. Here are some of the popular DevOps tools.
Git
Git is a distributed version control system that is highly popular for tracking changes in the code during the software development process. By efficiently managing code branches, facilitating version control, and providing a history of changes, Git allows multiple developers to collaborate efficiently on development projects.
GitLab, GitHub, and Bitbucket are a few popular Git-based platforms that provide collaborative environments for managing source code repositories. They facilitate version control, branching and collaboration among development teams.
Jenkins
Jenkins is a popular open-source automation server that facilitates CI/CD in a software development project. By automating the build, test and deployment phases, Jenkins allows organizations to deliver software rapidly and reliably. The plugin architecture supports various tools and technologies which enables users to seamlessly integrate Jenkins with diverse environments.
Jenkin’s web interface for configuring CICD pipelines makes it easy for developers to automate repetitive tasks, accelerate the delivery process and improve the quality and reliability of code.
Read the blog Gitlab vs Jenkins
Ansible
Ansible is a popular open-source automation tool for configuration management, task automation and application deployment. The tool uses a declarative language wherein users can define desired states for system configurations and orchestrate automation across various nodes in the environment.
Ansible is simple and easy to use. It is an agentless tool and communicates over SSH or PowerShell. To execute a task multiple times, users simply create a playbook under the source code.
Terraform
Terraform is an Infrastructure-as-Code (IaC) tool that enables organizations to define and provision the infrastructure using a declarative language approach.
It supports various cloud providers and on-premises environments. By describing infrastructure components in a simple human-readable language, Terraform delivers improved collaboration, high scalability and reproducibility of the infrastructure. Terraform allows organizations to implement agile and scalable architectures by streamlining infrastructure management and deployment automation.
Kubernetes
Kubernetes is a highly popular open-source container orchestration platform used for deployment automation, scaling and management of containerized apps.
This technology takes a declarative configuration approach to automate tasks such as scaling, load balancing and self-healing and simplifies container orchestration. It provides a robust infrastructure for organizations to run distributed, microservices-based apps across clusters of machines. Kubernetes brings consistency in application deployment across environments, scales well and helps in optimal resource utilization.
Read our blog DevOps Automation Tools to discover more tools!
Bridging the Gap: MLOps vs DevOps Similarities
Whether you call it MLOps vs DevOps or DevOps vs MLOps, they don’t compete with each other. Shared principles, overlapping tools and techniques and collaborative opportunities between MLOps and DevOps highlight the potential for a seamless integration of both practices for holistic and efficient software development and ML workflows.
a) MLOps vs DevOps: Shared Principles and Goals
Because MLOps is derived from DevOps, both concepts share similar goals and fundamental principles. For instance, the cultural shift towards breaking down silos between different teams and facilitating a more integrated and efficient approach to software development is a common agenda of MLOps and DevOps. Similarly, both emphasize collaboration and automation for delivering quality and reliable software or machine learning models quickly.
b) MLOps vs DevOps: Overlapping Tools and Techniques
When it comes to tools and techniques used in both MLOps and DevOps, there is a significant overlap. Continuous integration/continuous deployment (CI/CD) pipelines are a key component of both practices. Similarly, version control systems and infrastructure as code (IaC) tools are other examples of tools used in both domains. This convergence highlights how compatible and adaptable MLOps tools and machine learning workflows are, with DevOps practices.
c) MLOps vs DevOps: Collaborative Opportunities
MLOps vs DevOps provide collaborative opportunities for developers, operations teams and data scientists to adopt a unified approach to development and machine learning models. Together, they can streamline processes, leverage automation and enhance the overall efficiency of software development lifecycle and ML models.
They can also share best practices to benefit from each other’s experiences. This collaboration helps in continuous improvement across the entire development environment while ensuring consistency and accelerated delivery.
Differences Between MLOps vs DevOps
When it comes to MLOps vs DevOps, both share core principles but differ in terms of data-centric challenges, collaboration, model lifecycle management, performance monitoring, key tools and roles.
a) MLOps vs DevOps: Handling Data-centric Challenges
DevOps operates in a broader space compared to MLOps. DevOps focuses primarily on code versioning and continuous delivery. On the other hand, MLOps focuses on more data-centric issues such as data quality, versioning and feature engineering which are crucial to machine learning datasets and model workflows.
b) MLOps vs DevOps: Collaboration
While MLOps and DevOps emphasize collaboration between different teams related to development and operations, they differ in the nature of collaboration. MLOps focuses on domain-specific knowledge and promotes collaboration between data scientists, data engineers and other domain-related experts.
c) MLOps vs DevOps: Addressing Model Lifecycle Management
Both MLOps and DevOps efficiently address product lifecycle management. However, the key difference between DevOps vs MLOps lies in the end product. DevOps focuses on the application code lifecycle. However, MLOps is more specific towards machine learning model management. MLOps emphasizes the end-to-end lifecycle of machine learning models, covering versioning, experimentation and retraining of ML models.
d) MLOps vs DevOps: Performance Monitoring
In the MLOps vs DevOps, the debate MLOps focuses on continuous monitoring of model performance to detect and resolve issues related to concept drift and degradation over time. On the other hand, DevOps emphasizes application performance and infrastructure metrics instead of machine learning models.
e) MLOps vs DevOps: Key Tools
While DevOps vs MLOps show a significant overlap of tools, both differ in terms of the usage of tools. DevOps uses tools such as Jenkins and Kubernetes that are more related to traditional software development and deployment lifecycle. Organizations use MLOps tools such as DVC and MLflow for domain-specific tasks such as data versioning, model management and experiment tracking.
f) MLOps vs DevOps: Roles
MLOps introduces a few new roles that are not prominent in traditional DevOps environments. In MLOps work environments, you’ll come across roles such as machine learning engineers, data scientists and data engineers. As MLOps deals with data-related tasks and machine learning model development, the roles reflect this domain expertise.
Read the blog Hire DevOps engineer to learn more about a DevOps roles!
MLOps vs DevOps Comparison Table
Conclusion
MLOps vs DevOps share certain core principles and goals but differ in their focus and the challenges they address. While DevOps focuses on general software development and IT operations, MLOps adapts DevOps practices and tailors them to address complexities of machine learning development and deployment, data-centric processes and model lifecycle management. In addition, MLOps introduces specialized roles with domain-specific expertise in data science and model management and dedicated tools to meet the unique demands of machine learning workflows.
In conclusion, it is not about MLOps vs DevOps or DevOps vs MLOps. Both MLOps and DevOps promote efficiency and reliability in streamlining software and machine learning development processes.
MLOps vs DevOps FAQs
In DevOps, versioning is primarily applied to code. With MLOps, versioning is not confined to code but extends to datasets, artifacts and configurations. This approach allows organizations to reproduce machine learning experiments and track changes in models over time.
Yes, MLOps face challenges in CI specific to model training and validation. Managing diverse data sources, handling model versioning during CI and ensuring consistent results across different environments are a few unique challenges that are not typically encountered in a traditional DevOps environment.
The choice depends on the organizational requirements. If your organization works with machine learning model development and deployment, MLOps is good. On the other hand, DevOps suits organizations that focus on overall software development
