The MLOps concept helps optimize and automate AI/ML infrastructure maintenance, saving resources. Let’s break down the benefits and functionality of this approach, and observe the industries where it shows the greatest effectiveness.
According to a 2021 report by the IDC research company, when developing Artificial Intelligence (AI) and Machine Learning (ML) systems, companies spend most of their time processing data, and they simply no longer have enough time to create, train and deploy AI/ML models. As a result, this hinders successful project completion. The MLOps concept is designed to solve this problem. Let’s break down the benefits and functionality of this approach, and observe the industries where MLOps shows the greatest effectiveness.
What the MLOps is all about
Every ML model inevitably loses accuracy over time because the data used to train it becomes outdated and no longer reflects market conditions. As a result, the business gets the wrong analytical conclusions, which prevents it from responding properly to changes. To avoid this, you need to collect new data regularly, update the model, re-deploy it and integrate it with the infrastructure. The problem is exacerbated if a company needs to maintain multiple models. It is the MLOps approach that helps optimize and automate AI/ML infrastructure maintenance, saving resources.
MLOps is a set of tools and practices for continuous improvement of machine learning models. The concept uses regularly generated user data to automatically enable the existing models continuous training. The approach is based on the principles of DevOps — a methodology for building a business process of continuous delivery of a solution to the end user, which involves a combination of software development, launch and operation. A feature characteristic of both concepts is process continuity, namely reducing the wait between operations by automating and aggregating multiple data streams.
MLOps automates manual processes during data collection, model training and building. It eliminates the time-consuming repetitive steps required to keep the ML model up to date, and enables the team to quickly proceed to implementing machine learning in business processes. This helps save team effort and allows them to focus on the quality of the model.
One of the prerequisites for implementing the MLOps concept is the work of hybrid teams. Such projects involve Data Engineers, Data Scientists, ML Engineers, ML Architects, DevOps Engineers, and Artificial Intelligence experts. Preferably, all project participants should be equally proficient in competencies at the intersection of Big Data, Data Science, and DevOps. However, such experts are rare on the market. How much the roles and responsibilities of specialists are delineated depends on the size of the company and the scale of the task.
According to a Deloitte report, the global MLOps market will reach nearly $4 billion by 2025.
The tasks MLOps solves
Automating the ML models lifecycle
During the implementation of the MLOps concept, the Data Scientists team develops a pipeline, which is a separate internal software product. With its help machine learning models are created automatically on the basis of new information. This stage embraces data validation and its preprocessing, model training and validation of its performance. In this way, the entire lifecycle of machine learning models is managed automatically.
Continuous software updates
The practice of CI/CD, a fundamental recommendation within DevOps, ensures continuous integration and deployment of software updates at certain intervals. This makes the process of retraining the ML model automatic — the ML pipeline is restarted every time the code is updated or data is changed, which triggers new build and testing processes.
Continuous model training
The MLOps concept ensures the automation of model retraining. This allows for the algorithm to be updated at the first signs of model obsolescence or changes in the external environment. The need for automation of this process is due to the fact that in some areas the information is constantly updated: take, for example, an increase in the average value of real estate on the market or a change in the average rating of movies in a particular category.
However, it is important to note that such automation is not applicable to cases where new data attributes come up, or the data format changes. In such cases, the Data Scientists team has to completely update the machine learning pipelines.
Scaling ML applications
As the information used grows and the number of ML models managed increases, each system must continue to function efficiently and maintain the same level of performance. With MLOps, the scaling process is accomplished with minimal human intervention. At the same time, data growth improves the predictive capabilities of ML models.
Model performance governance
Professionals use a suite of tools to manage ML model performance, including logging, audit trails, and taking snapshots of the pipelines. They help control the operation of the pipeline, monitor model performance, and track the collection of analytics when implemented into business processes. It provides rich and timely insights for troubleshooting and fine-tuning model performance.
Where MLOps is particularly relevant
The MLOps concept is especially in demand among companies in dynamic industries that generate a lot of constantly changing data. These industries include retail and e-commerce, healthcare and insurance, among others. Let us briefly explain why automating the ML infrastructure maintenance is exceptionally important for these areas.
- Retail and e-commerce
Retailers process a large amount of data related to customer behavior, preferences and interests, customer traffic, market trends and other factors. As a result, businesses are in particular able to forecast demand, manage customer experience, and optimize warehouse and transport logistics.
In healthcare, ML is used to analyze patient data and the results of medical research, among other things. For instance, neural networks are trained on a large number of images from X-rays, ultrasounds, magnetic resonance imaging (MRIs), computerized tomography (CT) scans, and other checkups. Afterwards, the ML model recognizes and segments the types of diseases with the features that have already been studied before. Thus, the use of machine learning helps to reduce the fatal risks through early diagnosis and the proper choice of treatment. Obviously, in this case, it is critical to be able to adapt the ML models quickly and efficiently.
Insurance companies and departments analyze many variables when calculating possible risks, potential costs and revenues. Machine learning technology helps predict the risk of client default, the cost of an insurance cases, automate the coordination of treatment referrals, personalize offers and improve service quality.
* * *
The MLOps concept aims to accelerate AI and ML projects. This approach allows us to manage the lifecycle of AI/ML models. MLOps provides seamless integration of model training and integration of already trained models into software products. With this practice, the business gets the most up-to-date information about users and market conditions, which in turn enables effective management decisions.
Let us tell you more about our projects!