Why Machine Learning Solutions are Difficult to Implement without Machine Learning Operations?
According to Gartner, 85% of machine learning solutions fail because they use raw data. Data scientists work in isolation from operations specialists, and enterprises spend up to three months deploying an ML model. To solve these problems and reduce deployment time, DevOps development companies involve MLOps specialists in their projects. In this article, we will tell you what MLOps is and why businesses need to implement machine learning solutions.
MLOps as a new format of work
The standard software development life cycle (SDLC) includes requirements definition, design, development, testing, and deployment.
With the rise of ML and AI popularity, almost every company is incorporating voice assistants, chatbots, computer vision technologies, etc. into their applications. New product requirements and changes to the SDLC encourage organizations to use MLops in their CI/CD.
MLOps is an innovative format for working between data scientists and operations specialists. Like DevOps, it aims to automate the ML model development cycle so that the transition from model testing to production release occurs automatically.
MLOps is responsible for ensuring that any changes to an algorithm are automatically tested and deployed, even when the ML algorithm is in production.
The life cycle of ML models includes the following stages:
- formulation of a business idea;
- creating a machine learning model;
- testing and implementation of the model in the business process;
- use of the model.
The peculiarities of MLOps workflow
The workflow is based on the development cycle of an ML model. Several teams take part in it:
- business analysts determine the value that a machine learning model will bring to the business;
- data scientists collect and prepare information, based on which ML solutions are designed;
- MLOps engineers write the code for the machine learning model;
- DevOps professionals are responsible for deploying and monitoring models in a production environment.
Data scientists come up with an ML approach to solving business problems. They determine which algorithms and their combinations are necessary to train the model and how to evaluate their quality. As an outcome, they get an analytical report that acts as documentation for the project with descriptions, a clear structure, and examples. This report provides a clear understanding of how self-learning algorithms solve a business problem.
A data scientist communicates requirements to developers and DevOps specialists. With each of them, they discuss how to turn the requirements into a clear, automated, versioned ML pipeline that is easily scalable and includes feedback, monitoring, and reporting. Each department works with its part of the requirements and comes up with how to implement them in practice.
It’s a quite complicated picture. However, communication between departments becomes easier when a framework is built around the project, and the pipeline, part of monitoring, and integration are automated. New versions of the model come out faster.
What is MLOps based on?
MLOps includes the following components:
A model training pipeline. Data is extracted and processed so that the model has something to train on. A trained model is tested to understand its readiness for deployment.
A model registry. A new model is registered before being released into production.
Model deployment on an IoT device, inside an application, or a dedicated web service.
Model monitoring. MLOps engineers analyze the performance of the model. When they notice that the model starts to work inaccurately, they send it for retraining.
CI/CD orchestration. CI/CD tools implement a pipeline of model training, testing, and deployment.
So, just like DevOps services, MLOps is driven by continuous delivery, learning, and integration methodologies. The MLOps workflow differs from project to project. It depends on the business objectives of a product, the complexity of an ML model, the size of an organization, and other conditions.
Compare similar practices: MLOps, DevOps and DataOps
The MLOps process defines machine learning. It is different from similar DevOps and DataOps.
It’s fair to say that MLOps is DevOps for machine learning but they differ significantly. MLOps needs tools to save data and versions of models to test and retrain them. A model gradually degrades, so you need to constantly monitor its behavior. Testing in MLOps means continuous model training and verification.
DataOps is primarily responsible for the data life cycle. It may be part of MLOps but does not manage a model life cycle.
How MLOps contributes to the success of a business
To visualize how MLOps helps a business, let’s take a simple example. An online store wants to automate a chat to communicate with customers. Without MLOps, the bot will answer customer questions based on a specific dialog tree, and the effectiveness of such automation is low — 20–30%.
It is more efficient to implement an AI module developed with the help of MLOps. It will be able to:
- answer 60–70% more questions;
- “understand” non-standard customer requests;
- determine whether to ask a clarifying question or transfer the conversation to an operator;
- be automatically retrained by a data scientist (this does not require a group of engineers who regularly correct answer scenarios).
If we move away from the chat example and generalize, then MLOps speeds up the time of training and getting a model into production. The work is divided among the members of a project team, and data scientists do not need to deploy an ML pipeline themselves.
When an AI-based application runs without crashes or bugs, customers enjoy services. Thus, they are pleased to keep on cooperating with a brand.
MLOps guarantees more accurate predictions. Specialists regularly monitor the condition of a model. When the model drifts, it is sent for retraining. Therefore, a business receives more accurate information, based on which it makes important decisions without risks.
When a project needs an MLOps process
NewVantage Partners found that only 15% of top corporations have adopted machine learning technology and AI into mass production. The rest believe that AI is an expensive experiment with minimal payback. MLOps eliminates this myth. It makes it easy for machine learning development companies to deploy, track, and update models in production.
MLOps is needed for projects if the following problems arise when creating machine learning solutions and AI:
- models are sent to production slowly and with difficulties;
- a model has been working for a long time, but it has not been updated or monitored;
- it is expensive for an organization to regularly retrain its model.
Common misconceptions about MLOps
MLOps is a new approach, so many organizations do not have a clear understanding of this process. Ignorance breeds misconceptions that machine learning development companies struggle with.
Myth 1. MLOps is only pipeline usage.
MLOps is a broader concept than deployment. To make a pipeline function, much work needs to be done: integrating language frameworks with SDK frameworks, managing containers, controlling model versions, and connecting multithreaded processors and GPUs. In addition, you need to manage the API, control the load, be responsible for security, and much more.
It takes a whole team to do this kind of work. So, enterprises often outsource MLOps to ML development companies. Thus, they spend an average of 20% less money on infrastructure and 30% less time deploying the model.
Myth 2. ML involves the same processes as standard software development.
Standard software and an ML model are built differently and have different goals.
Machine learning is based on data. Modular code is built on data services and containerized microservices. For machine learning to work, you need a large number of versions, careful monitoring, and frequent deployments.
Machine learning is constantly improving. MLOps engineers use new languages or libraries that solve specific problems. Therefore, an ML codebase can include completely different programming languages.
Myth 3. A business takes a lot of risks when investing in MLOps.
Only those who organize MLOps incorrectly are risking. If you work with a team that knows how to effectively build a process, your investment will pay off.
Conclusion
Machine learning and artificial intelligence are cutting-edge technologies. According to Statista, 83% of companies that incorporate AI and ML into their processes have increased their budget within three years. Businesses note that AI automates critical business processes, improves customer relationships, and helps to fight hackers.
MLOps is important for projects that create ML solutions for business. Machine learning solutions development is different from building classic software. This requires processes, specialists, and resources. Not every organization has them. Machine learning outsourcing companies offer the best DevOps, DevsecOps, DataOps, and MLOps services.
