8 Differences Between MLOps and DevOps

Introduction

 

In the field of technology, you might have heard about Machine Learning a lot. It is said to be one of the biggest applications of Artificial Intelligence as it gives the computer the ability to learn things on its own, without being programmed. Automation has been proven beneficial for humans as they do not have to worry about the hassle of coding in order to enter every new information into the machine.

 

Machine learning can automate many human-driven tasks without the involvement of innate human intelligence. The machines are supplied high-quality data, and various techniques are employed to create ML models to train the computers on this data. The algorithm used is determined by the type of data and the action that has to be automated.

 

There are many applications of machine learning, but they can be primarily categorized into two applications- ML Ops and Dev Ops. Despite having similar fundamental roots, there are vast differences between these two.

 

Dev Ops is a traditional software-based approach that is concerned with developing and operating large-scale software systems whereas ML OPs is concerned with deploying Machine Learning and Deep Learning models in large-scale production systems. 

 

Let us learn in detail the meaning of ML Ops and Dev Ops and discuss the difference between them.

 

 

What is MLOps?

 

ML Ops refers to a set of strategies used by data scientists to develop and maintain machine learning models in the real world in an effective and reliable manner. Before releasing any algorithm, it is tested by data scientists, DevOps, and Machine Learning engineers to ensure that it is ready for production. 

 

If we had to draw a diagram, the relationship between MLOps and DevOps would look like this:

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Venn Diagram among MLOps and DevOps, 

Source: towards data science

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MLOps attempts to build a coherent system in which tasks such as data ingestion, assessment, deployment, model training, and so on are performed in unison. Without them, data scientists would have to perform everything manually, such as cleansing data, selecting appropriate models, and operating the whole infrastructure.

 

MLOps are quite similar to DevOps in a lot of aspects, conceptually. But once you dig deeper, you would realize the huge amount of differences as well. Before proceeding with the differences, let us learn a little about DevOps.

 

(Must read: Machine learning tutorials)

 

 

What is DevOps?

 

DevOps is a set of practices in the world of software engineering that helps in the development and operation of large-scale software systems. With the help of DevOps, it is now possible to convert any software to production within a span of a few minutes. 

 

Here programming, testing, and operational elements of software development are brought together to make sure the entire process runs coherently. DevOps is concerned with two concepts: Continuous Integration and Continuous Delivery. 

 

According to the source, there are seven steps for deploying any ML/DL model in production:

 

• Data extraction

• Data analysis

• Data preparation

• Model training

• Model evaluation

• Model evaluation

• Model serving

• Model monitoring

 

(Also read: Agile SDLC)

 

Let us now discuss the major differences between ML Ops and Dev Ops:

 

 

MLOps vs DevOps

 

According to the article written by towards data science, these are the major differences between MLOps and DevOps:

 

1. Experimentation

 

ML Ops are far more exploratory in nature than DevOps. This is due to the fact that with machine learning, developers have the opportunity to experiment and test various techniques to determine which ones perform best. 

 

Traditional software engineering methods, such as DevOps, are likewise experimental, but they are not fully integrated into the primary project. Typically, the software is built independently and then linked to the production model after transformation.

 

(Suggested read: Top trends in software development)

 

2. Involvement of Data

 

One of the most significant distinctions between traditional software and machine learning is that, although software development is only concerned with code, ML also incorporates data in addition to coding. 

 

Any Machine Learning model is created by running an algorithm on a large quantity of information. As you are aware, data originates from the actual world and is always changing. Code and data are two distinct entities that are difficult to reconcile. The role of ‘Data Engineering' enters the picture, assisting in the resolution of any ML-related production issues.

 

3. ML pipelines

 

Now that we have already mentioned Data Engineering, Let's talk about their requirements in ML. Data pipelines, which are a sequence of transformations that the data travels through between its source and finishing point, are a fundamental notion in data engineering.

 

Similarly, ML models frequently need some type of data transformation. These are controlled via data pipelines, which provide several benefits such as run-time visibility, code reuse, administration, and scalability. By including a few ML stages into the data pipeline, it is transformed into an ML pipeline. Because ML pipelines are simply based on code and are not dependent on data, they may be handled using a standard CI/CD pipeline, which is a basic DevOps technique.

 

4. Testing models

 

Any model must be tested before it can be deployed. Automation is tested in DevOps using unit tests and integration. Traditional software is easier to evaluate for model validation since it frequently produces correct and calculated results. ML models, on the other hand, are more difficult to evaluate because they do not offer a hundred per cent correct results. 

 

As a result, the nature of the tests must be statistical in character. In the case of DevOps, the outcomes are binary, either pass or fail. As a result, in the case of ML models, one must investigate the metrics and determine the acceptable values for model validation.

 

(Recommended blog: Types of Agile Methodologies)

 

5. Monitoring

 

Before putting any program to production, it is critical to collect monitoring data. Data handlers monitor standard metrics like latency, traffic, errors, and so on in order to gain control over any software's architecture. 

 

Monitoring ML systems is tough since they rely on data that cannot be controlled or altered. As a result, model prediction performance is assessed in ML models alongside other parameters. 

 

Another issue that arises while monitoring ML models is that there are no past models to compare them to because these models operate with new data every time.

 

6. Data validation

 

Any data pipeline is considered reliable when the input data is validated. These validations include file formatting, file size, column types, null or invalid values, etc. These are important factors to check in order to run the model smoothly. 

 

Higher-level statistical characteristics of the input should also be validated using ML processes. This is because if the average or standard deviation of a feature changes significantly from one testing data to the next, the trained model and its predictions will most likely be affected. 

 

7. Hybrid teams

 

ML Ops is operated with the help of Data engineering, DevOps engineering, and ML engineering. A data scientist alone would not be able to meet the requirements that would be necessary for ML Ops. 

 

Thus the team handling MLOps practices would be needed to have knowledge of all the three and would be called an ML Ops Engineer.

 

(Must catch: Machine Learning Algorithms)

 

8. Model and data versioning

 

Consistent version tracking is crucial for repeatability. In a conventional software environment, versioning code is sufficient since it defines all behaviour. 

 

In ML, we must additionally keep track of model versions, as well as the data required to train them and certain meta-information such as training hyperparameters. Models and metadata can be kept in a normal version control system such as Git, but data is frequently too big and changeable for this to be efficient or practical.

 

It is also critical to avoid coupling the model lifetime to the code lifecycle because model training frequently occurs on a separate timetable. It is also important to version data and associate each trained model with the precise versions of code, data, and hyperparameters utilized. 

 

The ideal answer would be a purpose-built tool, but there is currently no market consensus, and various methods are in use, the majority of which are based on file/object storage standards and metadata databases.

 

 

Conclusion

 

In this blog, you have learned different aspects of Machine learning models and their differences with traditional models developed through software engineering. Even though DevOps have fewer complications than MLOps, there are also fewer scopes for newer practices and improvement. 

 

Due to these limitations, MLOps nowadays play a huge role in any software model, as it gives the data scientists room to experiment. ML has advanced a lot in its field and is actively used in business solutions. 

(You can also read: Machine Learning vs Data Science)