In this rapidly evolving technological landscape, MLOps and DevOps play a critical role in streamlining processes and enhancing collaboration. While their prime objectives remain the same, they serve distinct domains with unique requirements and challenges.
As the demand for more efficient and swift software delivery increases, both MLOps and DevOps offer an extremely rewarding career for professionals to pursue. This article provides information about the components, similarities, and differences between MLOps and DevOps.â€
MLOps vs DevOps: What is DevOps?â€
In traditional setting, IT and operations departments of an organization work in isolation and collaborate only when requirements arise. However, in the DevOps setup, software (development) and IT (operations) units of an organization work as one cohesive unit.
DevOps work on collaboration, automation, and integration between software development and IT operations to improve deployment frequency, achieve a faster time to market, and ensure higher reliability.
MLOps vs DevOps: Phases of DevOps
The DevOps lifecycle has five phases devised to improve collaboration, automate workflows, and enhance the speed and quality of software delivery.
Planning Phase: Stakeholders discuss the requirements for the project to ascertain its feasibility and business impact, prioritize tasks and create a roadmap for its development.
Continuous Development Phase: Code is written in a guided and efficient manner in any language decided during the planning phase. With peer reviews and regular feedback, errors are identified at an extremely early stage of this phase.
Continuous Integration Phase: Since the code is developed continuously, it needs to be integrated, continuously, with the new systems to ensure changes are made available discreetly to all users during runtime. The integration phase also ensures that there are no errors during runtime.â€
Continuous Testing Phase: The quality assurance team continuously tests the developed software for errors and ensures that there are no errors in its functionality. All these processes are performed at predefined times
Continuous Deployment Phase: Code is correctly deployed on all the servers. During continuous deployment, configuration management and containerization tools are used.
Continuous Monitoring Phase: The operations team continuously monitors the application for any unusual activity, errors, and performance issues.
MLOps vs DevOps: What is MLOps?
If we intend to extend the principles of DevOps to machine learning and data science, we can achieve the same with Machine Learning with Operations (MLOps).
With MLOps, the deployment and operationalization of machine learning process is streamlined by taking the relevant models to production and then maintaining and monitoring them.
Data scientists, DevOps engineers and the IT department work in close collaboration to streamline the MLOps operations in an organization. Just like DevOps, in MLOps framework, the engineers deploy continuous integration and deployment (CI/CD) practices with continuous monitoring, validation, and governance of ML models.
The machine learning lifecycle consists of complex components like data ingest, data prep, model training, model tuning, etc. It also requires hand-offs across departments including data engineering, ML engineering, and data science.
MLOps encompasses all these processes and departments and ensures they work in tandem with close coordination to ensure smooth deployment, operationalization, and scaling of machine learning models. It ensures that these models are production-ready, reproducible, and maintainable.
MLOps vs DevOps: Components of MLOps
When we compare MLOps vs DevOps, just like DevOps, MLOps streamlines and automates the entire machine learning lifecycle. Here are the key components of MLOps:
- Model Training: It includes gathering raw data from various sources such as databases, APIs, or sensors, cleaning it, creating new features or selecting the most relevant features based on the raw data and choosing an appropriate algorithm to work on the data provided.
- Model Deployment: Wrap the model with all necessary dependencies, create containers that encapsulate the model, and setting up infrastructure required to deploy the model.
- Model Monitoring: Continuously track model performance metrics, identify changes in the model performance and log all model predictions and key metrics.
- Model Management: Track versions of datasets, code and models, keep detailed records of experiments and deploy models from the central repository.
- Data Management: Store large volumes of data in scalable storage solutions, implement policies to ensure data quality, security, and compliance, and track the origin and transformation from original source to final stage.â€
- Collaboration and Communication: Use tools for collaborative coding, version control and communication as well as maintaining comprehensive documentation of the ML pipeline.â€
- Automation and Orchestration: Use tools to automate repetitive tasks and manage the scheduling and execution of complex workflows.â€
- Security and Compliance: Use encryption, anonymization, and other security measures to protect sensitive data. Ensure compliance with regulations by implementing policies for data handling, storage, and processing.â€
- Infrastructure Management: Ensure that infrastructure can scale horizontally by adding more machines and vertically to handle increasing workloads.
Also read: ML Operations (MLOps): Streamlining ML Deployment and Management
MLOps vs DevOps: Similarities
Both MLOps and DevOps focus on making development and deployment steadier, cost-effective and better. So let’s look at some key similarities between MLOps vs. DevOps.
- Both MLOps and DevOps work on close collaboration between various teams including development, operations, and engineering to ensure smooth delivery of models and applications.
- Both MLOps and DevOps foster a culture of experimentation in organizations where they are implemented. New tests and approaches are tested quickly and implemented if they demonstrate benefits in terms of efficiency gains, cost reduction, or faster feature delivery.
- To ensure that models and applications work as intended, both MLOps and DevOps emphasize on close monitoring and feedback loops, swiftly identifying and remedying any flaw in the system.
- Both MLOps and DevOps pipelines work on code-validate-deploy loops in small batches to continuously validate and fix errors.
- Both MLOps and DevOps are streamlining processes. DevOps focuses on development and deployment of software and MLOps streamlines machine learning models.
- Both MLOps and DevOps work on close collaboration and cooperation between data scientists, system administrators, data engineers to develop common understanding between all stakeholders.
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MLOps vs DevOps: Differences
We know that DevOps enhances the collaboration and efficiency of software development and operations teams and MLOps takes care of deployment and maintenance of machine learning models. However, there may be quite a few differences between MLOps vs DevOps.
- Both MLOps and DevOps pipelines work on code-validate-deploy loops in small batches, however, with MLOps there are additional data and model steps that are required to build/train a machine learning model.
- In the traditional DevOps model, there is a software packaged in an executable that is deployed and validated through an automated process. In the MLOps model, the code trains a machine learning model. Training is validated against a test case and continues till the model delivers expected results.
- In DevOps, version control means to track changes only on the code of the file. However, with MLOps there are few other things to track including the dataset used, the code used to build the model, and the resulting model itself. All these details can be tracked with Model Registry.
- In DevOps, application monitoring can be performed through tools such as Grafana, ELK Stack and Datadog. With MLOps, the model that is training the data needs to be retrained continuously. As the data keeps on changing, so does the model that is training the data.
- In DevOps, software engineers write the code and DevOps engineers deploy and create a CI/CD pipeline. In the MLOps setup, data scientists perform the coding part and MLOps engineers take care of the deployment part.
MLOps vs DevOps: Which One Is Better?
Both MLOps and DevOps serve different purposes. Whereas MLOps is designed to train and manage the lifecycle of machine learning models. DevOps is designed to develop and deploy software applications.
In an MLOps setup, the data that is required to train the machine learning models keeps on changing. To keep pace up with these changing data patterns, machine learning models need to be consistently updated and maintained in response to changing data patterns.
On the contrary, in a traditional DevOps setup, the development and deployment teams work in close collaboration to ensure faster, accurate and reliable software delivery.
So if we compare MLOps vs DevOps, both are preferred in their specific development setup.
MLOps vs DevOps: Salaries
The average MLOps engineer salary in the USA is $162,500 per annum. Entry-level positions start at $133,014 per annum. Most experienced MLOps engineers can earn up to $200,000 per annum.
The average DevOps engineer salary in the USA is $130,000 per annum. Entry-level positions start at $110,006 per annum. The most experienced DevOps engineers can earn up to $165,640 per annum.â€
Interview Kickstart’s Offerings in AI/ML
If you’re a Machine Learning Engineer, Applied Scientist, or Data Scientist looking to revisit or master MLOps skills, you can take up our MLOps Training course and learn to master the entire lifecyle of MLOps. You will learn a range of in-demand tools, libraries, languages, and technologies used in machine learning.
Also, there is a machine learning course , which is specifically designed for those who like to train algorithms to identify patterns, make predictions, and solve complex problems.
Those interested in leveraging the power of Generative AI can opt for Applied Gen AI course that covers basic to advanced Gen AI concepts.
They can also opt for a more advanced Advanced Gen AI course for a deeper understanding of Generative AI.
FAANG instructors teach these courses in which they cover basic to advanced concepts related to the AI disciplines. Aspiring FAANG+ candidates also gain exposure to live projects to further enhance their skills.
These are some ideal tech interview prep courses for aspirants planning to pursue a career in AI.
Read the success stories of those who have successfully cleared interviews at top-tier tech companies in the world. â€
FAQs: MLOps vs DevOps
Is MLOps a Career to Pursue?
The career of MLOps Engineer is full of opportunities for personal growth, networking, and substantial rewards. However, the job comes with its own share of challenges, which if overcome, can yield rewarding results.â€
Does MLOps Require Coding?
MLOps requires coding knowledge to develop codes for management of data pipelines, model training, and deployment.
Can DevOps Engineer Upgrade to MLOps?
A DevOps Engineer can upgrade to MLOps if he or she has an experience of three years and has the knowledge of Devops practices, tools and techniques.
Is DevOps Easy to Learn?
DevOps requires knowledge and skills in software development, IT operations, and in some cases security, making it a cross-department, multi-disciplinary field. So, it can be challenging to learn especially for beginners.
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