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DevOps and MLOps are two diverse terms in the IT world that are interrelated and collaborative but produce different outcomes at separate stages in an organization.
DevOps “Development Operations” is an approach that uses a set of methodologies, tools, and technologies to automate, speed up, and enhance an IT organization’s application or software development life cycle.
Just as the name suggests, it is a technical ideology that involves collaboration between Development and Operations teams to produce effective IT products and results at a faster pace.
Typically, it is a continuous integration between the development and post-development procedures of a software or application lifestyle.
DevOps starts from the process of pre-development- planning, coding, and development procedures up to post-development processes that involve testing, release, deployment, operation, monitoring, and management.
Considering that the development procedure is over, most IT teams shift to new software product plans. The operation team continuously monitors the product and identifies bugs and other vulnerabilities. The development team further resolves those errors to enhance the operation of the software. This is possible in a continuous environment that performs software integration, delivery, deployment, and monitoring.
It should be noted that the Quality assurance team for code audit is taken into operations for an easy explanation.
The outcome of DevOps is usually a shorter code, fast deployment, error resolution, and enhanced operation of the IT product.
DevOps is not a one-time process that completes after IT product development. Instead, it is a continuous operation that requires team collaboration and assistance.
Continuous Development: Development procedures are still ongoing even after the software or an application is distributed among the users. The IT product needs to prove more than its competitors in the market and thus requires updates, improvements, and add-on features. The stage of planning and building the source code is never over for an IT product. This allows development teams to continuously build and update the code into a source code repository.
Continuous Integration: is the second procedure after the development of the IT product. Whenever a new feature or an update has been developed, it needs to integrate itself onto the source code repository and further interfaces. The continuous Integration process enables the DevOps team to add updates without hindering the operation of applications or software.
Continuous Testing: The quality assurance team performs code reviews on a regular basis to find out any vulnerabilities, errors, and compliances. The team performs Continuous Testing of the new updates and add-on features for any bugs.
Continuous Deployment: This stage embeds a code into a container after passing a series of tests and deploys it on the production server. This continuous process is automated by creating small deployments instead of huge chunks.
Continuous Monitoring: Continuous Monitoring is all about checking on the software to check the performance of the software and application. The DevOps team can automatically locate any possible errors, vulnerabilities, and attacks for effective operation.
Continuous Management: The DevOps team continuously manages the entire application or software, and its testing, maintenance, and quality management ensures that there is no downtime and its operation is consistent enough to match the business logic.
MLOps is a subset of Machine Learning and Operations. It is a crucial part of Machine Learning Engineering that standardizes and facilitates the process of Machine Learning and Artificial Intelligence models from the development to the production stage with maintenance and monitoring. MLOps marks an effective collaboration between Data Scientists and the DevOps team.
The MLOps Life Cycle involves the use of the existing data in an organization and its collaboration with Machine Learning that passes through DevOps stages. This is also a continuous process that forms an infinite loop of stages involving machine learning and software development to match the business logic of the models. The data is collected from the organization, transformed, and loaded in a warehouse to form models for effective machine engineering and development operations.
MLOps Planning: The project should be planned and well-documented in advance to specify the creation and use of machine learning models that meet the desired outcome in accordance with the business logic.
Data Collection: Data should be collected from the internal repository of an organization and external sources. The data is loaded into the pipeline for further processing and formulation.
Data Transformation: The obtained data might be unstructured and accessible in different formats. There might be gaps in the data with some missing values. The data should be completed, transformed, structured, and loaded into the warehouse for analytics and visualization.
Data Validation: The data is distributed over finite states for statistical models. It is necessary to validate data distribution models over time with limited evaluation and log maintenance for quality checks.
Effective Data Analysis (EDA): The most important step of MLOps is Exploratory Data Analysis (EDA) to extract features and standards of data sets and create visuals to group them in a pattern. It is an analysis procedure that is carried out before preparing a machine-learning model.
Continuous Model Training: Post the exploratory data analysis procedure, the models are formed by coding and development procedures for further testing and evaluation. These models are developed upon a logic that showcases the best-performing model for MLOps by automated training practices against the threshold.
Model Evaluation: When the models are prepared, they are tested for accuracy and performance and match business logic. Sometimes some of the models are rejected, and the process is repeated again until a required model is obtained to match the business logic of machine learning.
Model Deployment: The desired model that matches the business logic, efficiency, and statistics of performance is deployed in production to operate as per the set function.
Model Monitoring: After deployment, continuous monitoring ensures the streamlined operation of the model. It checks for any vulnerabilities and bugs that might hinder the operation. But most importantly, monitoring evaluates the model for business production outcomes as an old model might fail for new data.
| Parameter | DevOps | MLOps |
|---|---|---|
| Definition | An approach that utilizes a set of technologies and practices to automate and enhance development and operation of an IT product | A model that operates in production processes to deploy Machine Learning automation into software systems accessing the log data |
| Terminology | Development + Operation | Machine Learning + Development + Operation |
| Teams involved | Development, Quality Assurance, and Operation Teams | Data Scientists and DevOps Teams |
| Main Feature | Continuous Integration / Continuous Deployment | Exploratory Data Analysis and Continuous Model Training |
| Point of Focus (PoF) | Source Code Repository | Data Models |
| Lifecycle |
|
|
| Testing | Continuous Testing | Continuous Testing |
| Experiment | No experiments | Experiments |
| Usage of Statistics | No | Statistical Distribution of Data |
| Outcome | AI solutions | Efficient, and high performance softwares |
It can be clearly taken into account that DevOps is an approach to enhance the Lifecycle of an IT product expected to hit USD 20 Bn in 2026. While MLOps serves machine learning-based solutions to an organization using its own Model Pipeline and DevOps approach.
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