In this article, we will answer the main questions about the new phenomenon in the world of IT – MLOps, what challenges those who have decided to put work with data on stream and want to put the MLOps approach into practice will have to face, as well as trends and what awaits machine learning technology in the future.

What is MLOps?

I suppose there is no need to explain who DevOps is and what machine learning is. The news is that recently these concepts have been combined. 

Why DevOps in machine learning? As usual: it is easier to manage expectations, results, and iterations for clients; compare the results of experiments; recheck hypotheses, and implement developments. Of course, all these advantages do not arise by themselves. Big data is chaos, and machine learning is helpless without a set direction. Just the art of MLOps is to make ML work for itself. That is, to bring Operation into Machine Learning.

It’s not that easy. In normal enterprise development, you write a program and understand how to build CI/CD, how to deliver, and what are the release management options. In the Data Science world, you write a program that creates other programs. MLOps brings CI/CD practices, reproducibility, methods of monitoring results and control to this process.

The main task of MLOps is to create a common vision of the ML product delivery process, for which the team develops a model, what business value it carries, and what KPIs are worth. This allows you to track how Data Science metrics can affect business.

TOP 5 Challenges of Implementing MLOps

One of the problems of ML systems in real projects is associated with a decrease in the accuracy of online forecasts. Suppose there is a service in the banking ML system for detecting financial fraud that uses a machine learning model to analyze loan applications and make a decision about the likelihood of fraud. When a request arrives, the service must request information from the database about the current users, including their scoring score, the number of loan applications submitted over the past week, average income, and other features important for forecasting. In the case considered, the machine learning model requires knowledge of the latest facts about the user in order to make a correct prediction. 

When implementing data-driven management in corporate business processes, data storage and the possibility of operational integration are very important. In practice, many data owners store them in their own local repositories, which creates difficulties in the consistency of information and reduces the possibility of extracting valuable business insights from it.

Another problem of MLOps implementation is connected with different data stores – multi-cloud. The active use of cloud technologies, on the one hand, reduces the cost of providing IT infrastructure but complicates the development and consistency of various services.

The variability of data over time is characteristic of ML systems, but it is quite difficult to track and control. Especially when you need to track changes in the domain in time.

Finally, another important problem of MLOps implementation is data distortion. For each feature used to train Machine Learning models, the expected range of values and distribution should be recorded. When Data Scientists builds a model using this feature as input, they must also store information about how much each feature affects the output of this model. This information can be used to monitor objects for unexpected changes in cost or distribution that may invalidate assumptions made during the simulation.

If the value of the feature changes significantly over time, the performance of the model may decrease. As a last resort, if a damaged ML model generates this feature, it will stop working. Therefore, in practice, quality and performance control systems of ML models are needed.

What is MLOps for?

MLOps is not a panacea. Even if there is already a stack of technologies, we still need a mutual agreement of the team. If you start using an established cloud platform, it will not give the necessary result, because you need to discuss: how do we conduct experiments? How to make sure that the trained model corresponds to certain parameters?

It is especially worth considering the importance of the fairness of ML, ML must give the same conclusions, not based on biases, such as gender, skin tone, and so on. To do this, you often need to fasten additional pipelines.

In general, MLOps does not look like a magic automation button at all: you will have to sit down, figure it out, and think, and people from several teams will have to think at once. Well, you will have to communicate with customers: why they need machine learning, how they see it, and how they will use it.

What ML Trends Will Be Used Shortly?

With the advancement of technology, business models are being changed and improved. Lagging behind technological progress, it is not possible to remain afloat for a long time. At the moment, both the introduction of AI and ML technologies have already found use in a variety of business initiatives.

For example, this innovation is extensively used in such spheres as manufacturing maintenance, health services, agricultural production, and so on. These industries use IoT gadgets to track and predict the collected information. For instance, an IoT project utilizes machine learning to determine the presence of mosquitoes in real-time. This could lead to the development of early detection systems for disease outbreaks, for instance, by mosquitoes.

MLOps, in turn, will improve the ML experience, adjust it for the company or organization, and make the system as reliable and efficient as possible. Management is required as when dealing with massive amounts of data, the necessity for process automation increases. The system life cycle, as depicted by the DevOps discipline, is a key component of MLOps. MLOps can be an outstanding option for large businesses by reducing variability and making sure consistency and reliability.

Machine Learning: Move to the Future

Many sectors are already becoming increasingly advanced as a result of the scientific approach to ML and AI. In some instances, innovation helps them to stay able to compete, but using AI on its own can only get them so far. To truly gain a position in the market and split into an original thought science fiction future, we must bring innovation to accomplish our objectives in new and unique ways.

Each aim necessitates a unique approach. Communicating what is better for your business with specialists can assist you in comprehending which techniques, such as machine learning, can enhance your business’s effectiveness and enable you to achieve your view of supporting your clients.