What are ML Ops and Dev Ops?

Dev Ops refers to actions that minimize the software development lifecycle while providing continuous delivery and focusing on superior software quality. At the same time, ML Ops is a set of processes that aim to automate and productionize ML workflows and applications. Both models aim to use the software in workflows with high fault tolerance and repeat value, but ML Ops also comes with an ML component.

ML Ops share the same ethos as Dev Ops: having well-organized processes, automated workflows, modern tools for streamlining the development process, and robust deployments. The approach works well for software development, and ML Ops can be considered a subset that focuses on ML-specific projects and applications.

How Do ML Ops and Dev Ops Differ?

While the traits of Dev Ops and ML Ops are similar, they differ in many ways.

CI/CD

When it comes to the Dev Ops development cycle, it usually has a code that helps in creating an application. And this code is then moved to deployment after validation against some tests. The process is then automated until the final product is ready.

But in ML Ops, the code builds or trains an ML model. The final artifact will have data fed for producing insights. Moreover, the validation will check the efficiency of the trained model for test data. In ML Ops, the cycle continues unless the model can perform at a given threshold level.

Cycle

Ideally, both models include a code loop, then validate and deploy. But when it comes to ML Ops, it also contains additional modeling and data steps that will require the building or training of the ML model. So ML Ops will always have some nuances for every component that will be different from the traditional Dev Ops.

The ML Ops will include additional data transformation steps, labeling, feature engineering, and algorithm selection. Most ML algorithms are supervised, meaning the model has a target from which it can learn during training. Furthermore, data labeling refers to amalgamating the target with a set of data records, and the ML model will then start using it as a training set.

The data transformation or the feature engineering part is critical as the model will require specific data for meaningful results. The selection of the algorithm will depend on the prediction problem.

Monitoring

While Dev Ops include monitoring the application, within ML Ops, there is an additional aspect to monitor: the drift of the model. Data keeps changing constantly, so iterations of the model are also required. Training the data on an existing or older dataset will not help with future data. If you want to keep the model updated, it is critical to retrain it regularly.

Version Control

In Dev Ops, version control refers to tracking any changes to the artifacts or codes. But when it comes to an ML Ops track, there are many more items to track. Model training and building is an iterative process, and the metrics during each run must be tracked separately. Together, the training/testing data, the model code, and the artifact will form these components. Some metrics that are being tracked are model performance metrics like error rate and other hyperparameters.

Roles & Responsibilities

The team members’ roles differ in Dev Ops and ML Ops. While in Dev Ops, the software developers focus on the development of the code; the dev ops engineers will focus on the deployment. In ML Ops, the data scientists play a critical role as they will build the model and write the codes. ML Ops engineers will focus on deploying and monitoring the models while they move to production.

Why Data Makes ML Ops Different?

Every ML project is a software project, and each ML-derive application has some repository of codes beneath. When you ask a developer how the application operates, they will talk about dashboards and containers, the same for most software.

• Software engineers today have primarily defined processes that help them build software without hassle. But that’s not the case with the ML developers. So the question arises if there is a need to look at the ML projects like software projects and then use the software development best practices to educate ML practitioners.

• In a traditional software development environment, the engineer writes code to create a world for the software to operate. But in the case of an ML application, they start with some real-world messy data, which is hugely complex. The data is highly complex, challenging to understand, and hand-modeled. Because of these reasons, ML applications differ significantly from any other traditional software. Below are some more characteristics that make it different.

• ML applications must undergo experimentation cycles as they get constantly exposed to new data. The behavior of ML applications is learned through something other than logical reasoning; instead, we have to use our empirical observation to gain insights.

• Machine learning applications get exposure to the changing dynamics of the real world. In traditional software development, the surrounding world is simple and static and is created by the engineer.

• The skillset, roles, and responsibilities of people working on ML applications differ from those working on software applications. They focus on the data and experiments around it, which is invalid in traditional software development.

How to Make the Deployment Simpler?

Developers who focus on data-centric programming and have been working on R-Studio, Jupyter, Matlab, or Excel for modeling real-world data will be able to better understand the world of ML. Though these tools work great in insular environments and are ideal for prototyping, they need more in terms of production use and deployment.

To ensure that these ML applications are ready for deployment from the start, there is a need to adhere to and follow the same standards as other software. For continuous production deployment, there are more requirements for the ML application.

• Full versions of code, models, and data: Having robust versioning for all of these, along with the internal condition of the application, can help in many ways. It can help answer crucial questions like what happened in the back end, who did it and when it happened, and many others.

• When it comes to ML applications, the scale is more significant than in other environments. The data is comparatively larger, and the deep learning and ML models are much more extensive.

• It is critical to ensure the application is integrated into other surrounding systems. It will help in testing and validation in a better and more controlled way.

• All modern machine learning applications need careful orchestration as the complexity of these apps increases dramatically. There are several interconnected steps and much more. Thus, the developers have to orchestrate the application carefully for robust performance.

• While there is a need for large-scale applications, on the other hand, we also have a history of data-centric programming. Combining them for maximum benefit is critical as they cannot operate in silos. It is not possible to build an application on Excel. Similarly, having a large-scale business application with generic tools like Docker, Kubernetes, and others could be better.

There is a need to find common ground which will use data-centric programming for building large-scale applications and deploy to the latest infrastructure. And this is where ML Ops can be beneficial as it combines the data-centric method with modern production solutions.

ML Infrastructure: The Way Forward

The latest infrastructure is needed to help deploy and ensure maximum productivity for developing modern machine-learning applications. The primary layers of the infrastructure usually stay the same across projects.

• The foundational layer: It consists of three primary components, data, computing, and orchestration.

• The development layer consists of versioning for managing the dynamic environment and the architecture data scientists in the ML landscape build.

• The data science layer consists of components like the model operations, feature engineering, and model development layer.

Which One Should You Choose?

Both Dev ops and ML ops are different and similar at the same time. At the same time, one can start using them separately or in conjunction to improve the abilities of ML. One can use ML Ops for automating data analysis, while one can use Dev Ops for other tasks. Another example is using ML ops and additional tools that can help streamline the workflow.

So which one should you choose? It depends on the goals or needs of the application. If the scope of your work includes an ML project that will require continuous experimentation and iterations, then choosing ML Ops would be the right option. But Dev Ops would be ideal if you want to develop traditional software.

The best way is to choose the one that will be ideal for your team. You do not have to get a solution that will solve all your issues during the development; you can choose the option that would be ideal for your project.

Latest Trends in ML Ops

ML ops is a new term gaining traction recently in ML and data science. ML ops help in deploying and managing ML models. ML ops have different stages, from the initial development to the final deployment layer.

ML ops use specialist knowledge and tools to ensure the project’s success. Moreover, it also focuses on automated management of the model. It means that your team will use different tools to manage and automate the process rather than manually deploying the models. It helps speed up the deployment and production process while reducing the scope of errors.

To ensure success for your development project, adopting Dev Ops tools and principles is critical. And similarly, moving forward, managing the production deployment and development of ML models with ML Ops tools and principles will drive success. There are different flavors for ML ops, but choosing the right approach will be a must to ensure success.

Only time will tell how ML Ops develops further into the larger landscape, but it is also connected with Dev Ops. With time, ML will become a routine part of general software products. Moreover, Dev Ops and ML Ops can merge, integrating data versioning, pipeline training, and other activities into the existing stacks.