The similarities and differences between MLOps vs DevOps and how to harness their continuous feedback loops for faster delivery
The increased adoption of machine learning has had a butterfly effect on traditional approaches to software development. While DevOps methodology broke down the development and operation siloes of agile approaches, we are on the precipice of a maturation of DevOps to align with the addition of machine learning.
MLOps is emerging in response to the need for organizations to streamline the development, management, and maintenance of machine learning models. At Launchable, we’re helping to advance DevOps and MLOps strategies with machine learning by prioritizing testing efforts and ensuring that the most important tests are run first.
DevOps and the Intersection with MLOps
Development is always under the pressure to continuously release faster without sacrificing quality. While an agile approach helped achieve speed, organizations struggled uniting teams. Traditionally, development, operations, quality engineering, and security have had difficulty collaborating and communicating because of the nature of their divided workflows. DevOps methodology focuses on shifting siloed development cycles for faster, higher-quality releases.
What is DevOps: The down-low on DevOps Methodology
DevOps methodology strives to shorten software development life cycles and continuously deliver the highest quality software applications and products to clients and end users. The DevOps lifecycle serves as a framework and includes eight crucial phases.
Often represented in an infinite loop, DevOps teams plan, code, build, test, release, deploy, operate, and monitor continuously. The DevOps loop demonstrates the necessary communication and feedback needed throughout the entire software development lifecycle to make a project a success.
Like DevOps, MLOps has emerged from the need to unify Machine Learning and Operations.
MLOps: Machine Learning Operations
MLOps, Machine Learning Operations, is a practice aimed at improving the collaboration between data scientists and operations teams in the development, testing, and deployment of machine learning models. Its focus is to bring the principles of DevOps to the machine learning workflow.
MLOps aims to automate the deployment of machine learning models into software systems. MLOps helps organizations proficiently run their machine learning programs with an additional phase to the well known DevOps lifecycle. This addition focuses on machine learning requirements including identifying the relevant data and training the ML algorithm on these data sets to produce accurate predictions.
What is MLOps: Machine Learning’s Engineering Discipline
MLOps incorporates the prepping, training, testing, validating, deploying, monitoring, and retraining of machine learning models. By automating all steps of machine learning system construction, MLOps nurtures a collaborative culture, similarly leveraging DevOps best practices.
The MLOps phases may vary depending on the specific needs of the machine learning project - here are some of the general phases of MLOps.
Data collection and preparation
Data is collected and prepared for use in the machine learning model. This may involve tasks such as collecting data from various sources, cleaning and preprocessing the data, and splitting the data into training and test sets.
Model training
The machine learning model is trained using the prepared data. This may involve tasks such as selecting a model architecture and hyper-parameters, training the model using an optimization algorithm, and evaluating the model performance.
Model evaluation and validation
The trained model is evaluated and validated to ensure that it is accurate and reliable. This may involve tasks such as testing the model on a separate test dataset, analyzing the model performance metrics, and fine-tuning the model as needed.
Model deployment
The trained and validated model is deployed in a production environment. This may involve tasks such as integrating the model into the overall software development process, automating the model deployment process, and monitoring the model performance in the production environment.
Model maintenance and updates
The deployed model is continuously monitored and updated as needed. This may involve tasks such as collecting and analyzing new data, retraining the model, and deploying updated models to the production environment.
Differences between MLOps and DevOps
MLOps is similar to DevOps in that it aims to streamline the collaboration between different teams and facilitate the continuous delivery of machine learning models. However, there are seven key differences between MLOps and DevOps in overall execution and within specific phases of their continuous cycles.
Focus & Goals
MLOps focuses specifically on the integration and management of machine learning models, with the goal of improving the efficiency and effectiveness of machine learning workflows.
Development & Version Control
MLOps development refers to the code that builds and trains a machine learning model. MLOps version control is centered around tracking the changes made during the building and training of the machine learning model.
Monitoring
MLOps monitors risks within the machine learning model to eliminate data drift and model accuracy defects.
Despite their similar goals and differing elements, MLOps is best regarded as an implementation of DevOps that focuses on machine learning applications. As MLOps adheres to DevOps principles, it helps maintain seamless collaboration between the development and deployment of machine learning models, making it possible to handle large-scale data projects.
Incorporating Predictive Test Selection into your MLOps Strategy
For teams interested in harnessing the power of machine learning, building machine learning models requires resources and time. But at Launchable, we’re making it easier for teams to incorporate machine learning into your test cycles without heavy manual lift.
Within your DevOps and MLOps feedback loops, there lies a tsunami of data from your test suites. That data is ripe for advancing your bloated testing cycles and speeding up your development lifecycle.
Launchable’s Predictive Test Selection speeds up the software testing process with a machine learning model that identifies and runs tests with the highest probability of failing based on code and test metadata. Designed to predict which tests are most likely to fail in the shortest amount of testing time, Launchable’s machine learning model validates changes faster in four steps.
ML Model Training
Every model is trained using a mass of extracted metadata from your own test results and code changes over time
Test Selection
With a trained model you can start requesting dynamic subsets of tests for your builds. It looks at your test suite, changes in the build being tested, and environments.
Test Prioritization
The model prioritizes tests based on the factors including test execution history, test characteristics, change characteristics, and flavors.
Subset Creation
The prioritized test list is combined with the Optimization target to create a subset of tests. This cuts the prioritized list into two chunks: the subset, and the remainder.