Machine learning is a jargon that practically everyone in the IT industry has heard of, and we are past the stage of showing its worth in 2022. The challenge today with most businesses is implementing and deploying a successful machine learning project at the organization.
DevOps is an agile methodology that combines programming, testing, and operational aspects of the software development cycle. DevOps aims to rework what has frequently been segregated procedures into a logical and unified process that can move forward with a few hiccups.
Enter MLOps! MLOps is a collection of methods that combines Machine Learning, DevOps, and Data Engineering to ensure that Machine Learning systems are dependable and data-centric in production.
DevOps and MLOps attempt to put software in a repeatable and fault-tolerant workflow, but MLOps also adds a machine learning component to the mix. MLOps is a subset of DevOps dedicated to machine learning applications and projects.
Even though DevOps is considered aparent of MLOps, there are several contrasts between the two. Let's have a look at these for a better understanding.
A code-validate-deploy loop is there in both DevOps and MLOps pipelines. The MLOps pipeline, on the other hand, includes extra data and model stages that are required to construct and train a machine learning model. As a result, MLOps has a few differences from traditional DevOps for workflow components.
Software developers are responsible for writing the code, whereas DevOps engineers are responsible for deployment and setting up a CI/CD pipeline. In MLOps, data scientists take on the job of application developers, writing the code that allows the model building. MLOps engineers are in charge of deploying and monitoring these models in the field.
Code is the only variable in the case of DevOps, and the code version control ensures that any changes to the product are structured and documented clearly. In MLOps, the code isn't the only variable in machine learning. Data, parameters, meta data, logs, and the model, are crucial inputs to be managed.
To retrain and implement a DevOps methodology, all you need is a single CI/CD pipeline. In MLOps, a multi-step Pipeline is required for automatic retraining and deployment of a model.
The amount of freedom you have to experiment and test to discover which strategy gives the best outcome is one of the most significant differences between MLOps and DevOps. It's normal for data scientists working on machine learning projects to try one technique to solve a problem and test another a few days later.
Traditional software engineering also requires some experimentation, although it is usually brief and done apart from the main project. It's rare to see a developer devote a week's effort to one route, then, for the sake of it, to pursue another.
On the traditional DevOps side, you'll usually have code that generates an application or interface. The code is in the form of an executable (object), deployed, and tested against a set of criteria. This procedure should ideally be automated and continue until a finished product is ready.
In MLOps, on the other hand, the code is used to create/train a machine learning model. The output artifact, in this case, is a serialized file that may be fed data and generated inferences. Validation would entail comparing the training model's performance against test data. Similarly, this cycle repeats until the model achieves a specified level of performance.
The difference between DevOps and MLOps monitoring is that software does not deteriorate, but machine learning models do. When a model is in production, it starts making predictions based on new data it receives from the outside world. As the business environment changes and adapts, this data will alter, resulting in model degradation. MLOps includes techniques for continual monitoring and retraining of algorithms for continued use in production.
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