The IT sector is quickly adapting to artificial intelligence (AI) and machine learning has a significant role in this rapid improvement. Nowadays, we hear words like ML, ML algorithms, and ML engineering.
But it’s not just a fancy term that companies adopt to appear trendy. The impact of machine learning is potent and its transformation of the IT sector is changing the way we look at modern businesses.
Whether it’s the eCommerce industry, marketing, education, or healthcare, machine learning has shown its impacts everywhere.
Machine learning seems interesting to implement because programs learn from examples (dataset), and improve themselves on their own. That said, the learning process may take a while if there were no guidelines or best practices to follow. As technology became more common, it gave way to a more sophisticated approach such as the MLOps. Also referred to as machine learning DevOps, MLOps is about adding discipline to ML model development and deployment.
But since the term is relatively new, it’s easy for new learners to confuse it with the traditional DevOps. So, let’s clear the confusion in this post. We will highlight the significant differences between ML DevOps and DevOps. It should help developers understand the theoretical and applied distinctions between the two.
What is DevOps?
DevOps is used to ensure the smoother development and deployment of large-scale software. DevOps engineers are experts in converting software to production in just a few minutes.
In DevOps, there is immense focus on combining testing, programming, and operational features of the software and running them coherently. DevOps primarily focuses on Continuous Integration and Delivery (CI/CD).
Machine learning ops can be similar to DevOps primarily because both are intended to reduce the development cycle. For instance, selecting the suitable ML models, data ingestion, deployment, and assessment are some essentials that coincide with DevOps.
What is MLOps?
Data scientists use different strategies to develop the ideal machine learning models to address real-world problems. These strategies are aimed to provide more efficient and reliable methods and solutions to the users.
To develop machine learning models, data scientists work in collaboration with operations professionals. Obviously, the model undergoes rigorous testing in different stages of development, which may require troubleshooting and quick fixes.
By following standard practices, these teams can collaborate effortlessly to reach the final stages of development, and MLOps serves this purpose.
MLOps or machine learning operations is a set of best practices for improved communication between the two teams. These practices help in:
- Making the management process easier
- Automating the deployment of ML models on an industrial scale
Significant differences between ML DevOps and DevOps
While ML Ops and DevOps have many resemblances, the critical differences between the two fields can be observed in the following areas:
1. Implementation During the Experimentation Phase
Like machine learning models, MLOps are easily integrated into projects as an experimental step. Since the idea is to help the machine learn on its own, developers are comfortable trying different techniques and experiments at such early stages of the project. Ultimately, they can figure out the best ones and continue developing them further.
The same isn’t consistent with traditional DevOps. Even though DevOps are experimental, they are never fully integrated into the projects. Instead, DevOps are primarily built separately as software and then integrated into the production process.
2. Development Cycle Duration
MLOps and DevOps follow a standard code-validate-deploy loop. However, since machine learning involves intense data modeling and training, you can’t keep them out of the development cycle.
MLOps focus on automating the training processes, making it easier to integrate and deploy the algorithms into production much quicker. As a result, MLOps differ in their lifecycles and workflow.
3. Validating Data
Input data validation is a critical element for any data pipeline. For instance, input formats, size, types, etc., are the defining features of any input type. With robust data validation models, the operational models run much smoother and enhance the overall quality of the software.
With MLOps, it is much easier to validate high-level statistical data, thanks to various averaging and standard deviation techniques. In DevOps, model validation is possible through unit testing.
On the other hand, MLOps output a statistical model performance rate which is generally a percentage error.
4. Development Methods
MLOps and DevOps perceive development in different ways. Perhaps, it’s one of the biggest distinguishing factors between the two. MLOps focuses on building code that will train a machine learning model.
These models will integrate further and have data fed into them to produce new computations. Since most ML models run on supervised learning, the developers then validate the output against the test data.
On the other hand, DevOps is about developing code that will serve an application. The test code is then deployed with a good-looking UI and scrutinized for performance. Until the desired product is achieved, the testing process should continue.
5. Version Control
Version control has a whole different meaning in MLOps. A typical DevOps version control focuses on tracking changes in the code. However, MLOps also keep track of other parameters from experimental runs.
When you use operational machine learning to train a model, there may be multiple components and metrics to track before achieving the desired results. It’s a sophisticated tracking process that allows you to recreate the code for auditing purposes.
Generally, components like datasets, model builders, model artifacts are tracked for smoother version control in MLOps.
6. Data Monitoring
Every development cycle relies on comprehensive data monitoring. So, developers use data handlers to monitor traffic, latency, errors, etc. The monitoring allows them to have more control over the software architecture.
However, machine learning models are not all that simple because you’re often working on real-time systems. So, there is very little control over the data, so you cannot control or manipulate what goes into the system.
Also, since ML models continue to upgrade with every dataset, it’s virtually impossible to work with the past models to compare performance.
7. Developer Roles
As a software engineer, you can expect varying roles depending on whether you work as a DevOps or MLOps engineer. If you are a DevOps engineer, your responsibility is to deploy the code developed by the software team.
Contrary to that, the applications are developed by a data scientist. They write codes and build the models. Then, ML engineers deploy these models and monitor the performance during production.
There may be very little difference between ML DevOps and traditional DevOps from the outset. However, deeper technical differences mean that MLOps and DevOps are two separate niches.
So, it’s a good idea to seek help from ML model deployment tools like Verta to make your development cycles more efficient and error-free.