Until recently, an integral part of the training was exposure to the standard software development life cycle (SDLC). This would start with requirements analysis, and then be followed by planning, engineering and design, development, testing, deployment, and finally maintenance. Many learn waterfall, iterative, and agile software development models also.
Almost every organization is trying to implement machine learning and artificial intelligence (AI) in their business. The growing need to create machine learning systems complements some of the principles of SDLC, which eventually form a new engineering discipline called Machine Learning Operations, or MLOps.
MLOps is an engineering discipline that aims to unify the development (dev) and deployment (ops) of machine learning systems in order to standardize and optimize the continuous delivery of high-performance models to production.
Simply put, it’s a way to take the pain out of the development process and make it easier to deliver machine learning-based software, not to mention streamlining work for every team member.
Historically, data science teams dealt with tangible amounts of data and very few models on a small scale. Now the situation is changing, and businesses have to implement decision automation in the widest range of applications. And of course, this goes hand in hand with a whole host of technical challenges that come with building and deploying machine learning-based systems.
To better understand MLOps, we first need to look at the lifecycle of machine learning systems, which usually involves engaging several development teams at once, covering different aspects of working with data. This often involves:
Several teams at Google have done extensive research on the technical challenges involved in building systems based on machine learning. The NeurIPS article on Hidden Technical Debt in Machine Learning Systems clearly demonstrates that model development is only a small part of the whole process. There are many other processes, configurations, and tools that need to be integrated into AI systems.
In order to optimize this whole system, a new engineering culture of machine learning was formed. And it involves everyone from top management with minimal technical skills to data scientists, DevOps, and machine learning engineers.
When managing systems on a large scale, there are many bottlenecks that need to be taken into account:
According to Forbes, the market for MLOps solutions will reach $4 billion by 2025. Not surprisingly, data-driven analytics is changing the landscape of all market verticals. The value of AI in the US agricultural market, for example, is projected to be 2629 million for 2025, which is almost three times more than it was in 2020.
To illustrate this point, let’s recall two important rationales for implementing machine learning: multiple parameters and the ability to solve conceptual problems. Machine learning models can provide many features, namely:
MLOps is designed to manage all of these tasks.
However, it also has its limitations, which we recommend considering when producing ML models:
Meanwhile, MLOps is especially useful when experimenting with models that use an iterative approach. MLOps is ready to go through as many iterations as needed since machine learning is experimental. This helps to find the right set of parameters and create reproducible models. Any change to data versions, hyperparameters, and code versions results in new versions of the deployable model that allow for experimentation.
Each machine learning project aims to build a statistical model from data using a machine learning algorithm. Therefore, data and machine learning models are two different artifacts for software development in terms of code development. In general, the machine learning life cycle consists of three elements:
Because machine learning introduces two additional elements into the software development lifecycle, things get more complicated than when using DevOps for any software development. While MLOps still strives for version control, unit and integration testing, and continuous package delivery, it brings some new differences compared to DevOps:
The level of each stage of data engineering, model engineering, and deployment automation determines the overall maturity of MLOps. Ideally, the CI and CD pipeline should be automated to define a mature MLOps system. So there are three levels of MLOps, divided into categories and based on the level of process automation:
Unlike DevOps, model reuse is a different story as it requires data manipulation and scripting as opposed to software reuse. Since the model decays over time, it becomes necessary to retrain it. In general, data and model versioning is the “code versioning” in MLOps, requiring more effort than DevOps.
To think of a hybrid MLOps approach for the team that implements it, it is necessary to evaluate the possible outcomes.
Pros of MLOps:
It can take some time for any team to adapt to MLOps and develop their way of working. Here is a list of possible stumbling blocks to look out for:
MLOps costs:
As you can see, MLOps has a lot of technical capabilities. As a result, each company must develop its own set of practices to adapt MLOps to its AI development and automation. We hope that the mentioned guidelines will help you smoothly implement this philosophy in your team.
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