As you may be aware, DevOps attempts to bring together programming, i.e. the development of a web app or any software, testing, and deployment, where MLOps have similar goals as well. We are attempting to streamline the entire machine learning model development life cycle. Machine learning has begun to be integrated into major projects as it has become more applied and less theoretical. So, in MLOps, we are attempting to stitch this lifecycle together in order to create a coherent process with no more obstacles.
We’ve passed the point of proving its worth in 2022. The question now is how to properly implement and confidently launch a successful machine learning project into production. In this article, we will look at two concepts: MLOps and DevOps. We’ll start with their fundamentals and then go over the differences between them.
- What Is MLOps?
- What is DevOps?
- MLOps Vs DevOps
What Is MLOps?
ML Ops is a set of strategies used by data scientists to effectively and reliably develop and maintain machine learning models in the real world. Data scientists, DevOps, and Machine Learning engineers test algorithms before they are released to ensure that they are ready for production.
MLOps—the term itself derived from machine learning or ML and operations or Ops—is a set of management practices for the AI(Artificial Intelligence) lifecycle. To understand MLOps first, we have to understand the AI(Artificial Intelligence) lifecycle for production to solve the data-driven decision-making problems as shown below, where different teams like Product team(Business Development), Data Engineers, IT, or DevOps are involved in this life cycle.
The Key Phases in MLOps are from the above diagram:
- Data Collection
- Data Ingestion
- Data Analysis & Curation
- Data Labeling
- Data Validation
- Data Preparation
- Model Traning / Model Development
- Model Evaluation
- ML System Validation
- ML System Development
Machine Learning Operations (MLOps) practices and technology provide a managed, scalable means of deploying and monitoring machine learning models in production environments. MLOps best practices enable businesses to successfully run AI.
MLOps is modeled after DevOps, the existing practice of writing, deploying and managing enterprise applications more efficiently. DevOps began as a way to bring together software developers (the Devs) and IT operations teams (the Ops), destroying data silos and enabling better. It provides a clear direction for organizational benefits to enable critical capabilities such as simplified deployment, machine learning monitoring, life cycle management, compliance.
What is DevOps?
DevOps is the practice of collaborating to build and deliver software as quickly as possible. Through collaboration and iteration, DevOps enables software developers and operations teams to accelerate software delivery. Communication is improved by using the DevOps methodology. Individuals and teams can benefit from a DevOps culture and practice by having faster development life cycles, higher deployment velocity, and better code quality through testing.
DevOps Application Cycle includes the following:
We can understand the entire DevOps process from the diagram above. Organizing tasks, schedules, and other items begin with this step called planning. If you use agile methodology, planning begins with the user stories created during each sprint. The development or coding of the software then begins. The application developed thus far is tested for bugs. Once the code has passed this stage of testing ( continuous integration/Continuous Delivery), it is ready for deployment. The next step is for Operations (Operate) to maintain infrastructure and truncate any software vulnerabilities or security issues. The final stage is to monitor the application developed in order to fix any hiccups and ensure a smooth end-user experience.
MLOps Vs DevOps
The Difference between MLOps and DevOps are:
We can see the difference in Systems. They are Software Based systems and Machine Learning systems as shown below for versions.
Code version control is used in DevOps to ensure clear documentation of any changes or adjustments made to the software being developed. However, with machine learning, the code isn’t the only variable. Data is another critical input that must be managed, as are parameters, metadata, logs, and, finally, the model.
For software system projects , the build time is irrelevant , so hardware on which the projects done is also irrelevant.When we training models of machine learning, specially for deeplearning, tend to be compute very intensively.Like for larger models , we take hours to weeks to train.So MLOps need to be setup to be much more depenends on what kind of GPU machines, Could vendors to manage.
In MLOps, you must not only test the code but also ensure that data quality is maintained throughout the machine learning project life cycle.In MLOps, you may not always be deploying a model artifact. A machine learning pipeline that includes data extraction, data processing, feature engineering, model training, model registry, and model deployment may be required for the deployment of a machine learning system.
Continuous Monitoring and Retraining
Continuous retraining is the concept in MLOps that does not exist in DevOps . This step involves automatically identifying scenarios/events that necessitate re-training and re-deployment of a model into production due to performance degradation in the currently deployed machine.While monitoring in DevOps the software does not degrade but machine learning model do because the model is deployed into production, it start to predict from new data coming from real world. Here data will be continuously changing as per business requirements , thus results model degradation.So, that’s why Machine Learning models need continuous monitoring and retraining.
MLOps are far more exploratory in nature than DevOps. Because with machine learning, developers can experiment and test various techniques to see which ones perform the best model for predictions to generate good solution for data-driven and decision making problems
In software-based system projects , DevOps are also experimental,but they are not fully integrated into the primary project. Where, the software is developed independently and then linked to the production model after transformation.
In this article, You’ve learned about various aspects of machine learning life cycle steps involved and how they differ from traditional models developed through software engineering. DevOps has fewer complications than MLOps, but it also has fewer opportunities for newer practises and improvement.
As a result of these constraints, MLOps now play a significant role in any software model, as they allow data scientists to experiment. ML has made significant advances in its field and is now widely used in business applications.
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Also Read about : Regularization in Machine Learning.
Data Scientist with 3+ years of experience in building data-intensive applications in diverse industries. Proficient in predictive modeling, computer vision, natural language processing, data visualization etc. Aside from being a data scientist, I am also a blogger and photographer.