The global economy is expected to grow by $15.7 trillion by 2030, and the GDP of local economies will grow by 26% thanks to artificial intelligence. To switch to this technology, organizations spend from a month to several years. You will be able to implement changes faster and get benefits if you automate artificial intelligence with DevOps. Let’s see how DevOps for AI works and what benefits it brings to business.
DevOps and AI
Traditional infrastructure is not capable of deploying artificial intelligence and machine learning. To do this, you need to create a separate AI/ML pipeline, which is more often called MLOps.
MLOps is a new format of cooperation between businessmen, researchers, mathematicians, defense specialists and IT engineers for the development of artificial intelligence systems. In other words, it is a useful tool that helps to use ML and AI to solve business problems.
The term MLOps is analogous to DevOps, but refers specifically to ML and AI technologies. DevOps automates and accelerates the deployment of an artificial intelligence model, rather than a traditional application. At the same time, standard approaches are used: continuous development, testing and delivery. Therefore, it is necessary to store, transmit and process voluminous data arrays.
The life cycle of machine learning models is similar to the software development lifecycle. They differ in that the model algorithms are generated by ML tools. Therefore, engineers came up with the idea to adapt well-known approaches in software development for machine learning models. In turn, the development of an artificial intelligence model includes the following stages:
- defining a business goal,
- model training,
- testing,
- integration in the business process,
- use of model.
When the working model needs to be trained with new data, the cycle starts again. Then the model is finalized, tested, and then a new version is released.
DevOps tools accelerate the life cycle of the model: automate testing, delivery and tracking, as well as design the calculation of models in the form of separate microservices.
How DevOps for AI works in practice
MLOps can be successfully used to solve business problems, such as a robotic chat of a banking application. Usually the user asks some questions in a message and receives an answer that is predefined in the dialog tree. To automate such a chat, it is necessary to collect the rules set by experts. But they are quite difficult to develop and maintain, so the efficiency of such automation can be only 20-30%.
A more profitable solution is the introduction of an artificial intelligence module created with the help of machine learning. The ML model is capable of performing the following actions:
- process more client messages (up to 80%),
- better determine user needs even by fuzzy text formulations,
- effectively determine whether it is necessary to ask clarifying questions or switch the conversation to the operator,
- automatically train and retain (a Data Science specialist usually helps with this).
The number and variety of tasks that are solved with the help of machine learning and artificial intelligence technologies is growing rapidly. According to Gartner estimates, by 2022 each company will have about 35 projects in the field of AI. Enterprises save on automating processes, for example, when there are fewer operators in the call center, and banks do not need to check and sort documents manually. Companies are adding new convenient features based on artificial intelligence to expand the base of satisfied customers.
Who works with the AI model
A data processing and analysis specialist should develop an architecture, program a model based on it, prepare data and provide the application itself. As you can see, this is a large-scale job for one person. Thus, the specialist often works with programmers and DevOps engineers. That’s why MLOps tasks are solved using standard DevOps tools familiar to various IT professionals.
The choice of employees who will work with DevOps for artificial intelligence depends on the scale of the enterprise. But remember that the quality and speed of creating models depends on process organization and personnel.
How DevOps scales AI technologies
Artificial intelligence delivery methods are constantly changing. DevOps solves this problem and allows you to scale AI by connecting ML models at all stages: from design to production. So, DevOps for artificial intelligence has the following advantages:
- speed of market entry — with the introduction of artificial intelligence, non-value-added operations are reduced,
- improved quality — continuous learning based on DevOps constantly improves artificial intelligence models,
- stability — continuous monitoring guarantees the reliability and accuracy of the system.
Let’s take a closer look at how engineers create AI models.
Step 1. Define a business task.
It is important to understand what problem the machine learning model will solve, and calculate what benefits the business will receive by implementing MLOps in its processes.
Step 2. Collect data.
Model accuracy depends on the size and quality of the dataset on which it will be trained. Without working in accordance with the principles of DevOps, this stage takes up to 70% of the time to create a model. Ultimately, all the work, such as data extraction, cleaning, labeling and verification, will be done by engineers manually.
DevOps for artificial intelligence automates time-consuming tasks, so that data pipelines process more information. Accordingly, engineers can devote time to developing an AI model. As a result, in a shorter period of time, specialists receive high-quality data sets that can be immediately put into operation.
Step 3. Create a prototype of the model.
This step can be implemented simultaneously with the previous one, because it also takes a lot of time for experts to develop functions, choose an algorithm and train a data set. Usually one-time training is not enough, and you need to go through several rounds to improve the model.
DevOps accelerates the creation of an artificial intelligence model thanks to a flexible infrastructure and the capabilities of parallel development, testing and model versioning.
Step 4. Integrate the model into the system.
If your company approves the model, it is implemented into a specific business process. Without DevOps, this procedure is difficult, especially when several data scientists deploy a disparate model on their local workstations.
DevOps makes artificial intelligence models portable and modular. Thus, they can process incoming data streams in real time on scalable and distributed platforms.
Step 5. Track AI models.
It is necessary to monitor the artificial intelligence model so that there is no “drift”. Over time, the data or model itself may become outdated. In order for the model to be relevant, it needs to be constantly trained.
DevOps is responsible for continuous AI training. This concept allows you to track indicators such as “drift” and “accuracy” so that the model remains relevant for a long time. Thanks to the symbiosis of DevOps and artificial intelligence, high-quality and in-demand software solutions appear on the market.
Conclusion
DevOps helps speed up data collection by improving model development and AI scaling.
Rashly implementing AI is not worth the effort. What works successfully in other businesses may not suit yours. If the cost of equipment and tools turns out to be more expensive than the estimated profit from the technology, put this idea aside. It is important to recognize such situations at the early stages of creating artificial intelligence systems.
If your goal is to implement artificial intelligence, experts from Andersen — a DevOps developer company — will help you with this issue. Feel free to leave a request on our website and we will give feedback quickly.