What is MLOps and Why Do We Need it?

To keep up with the demands of modern applications, it has become a norm to implement various tools and practices in software development. With the most recent buzz around machine learning and artificial intelligence, DevOps is getting more and more sophisticated to build fast and efficient applications for modern users, machine learning software is also not left out in this trend. When talking of integration of Machine Learning or Artificial Intelligence in DevOps, two terms come to mind; Machine Learning Operations (MLOps) and Artificial Intelligence for IT operations (AIOps). While both terms sound very similar, some characteristics clearly distinguish them.

What is MLOps?

MLOps definition refers to the application of DevOps processes, practices, and techniques to machine learning projects. Through MLOps, DevOps practices such as collaboration, source control, testing, continuous integration, and automation are applied to machine learning projects right from the design to training and deployment.

AIOps, in contrast, is the application of machine learning and artificial intelligence to DevOps processes to help improve the various aspects of the DevOps processes. AIOps is basically applying machine learning to DevOps, while MLOps is applying DevOps principles to machine learning project workflows. There is also a clear distinction in AIOps/MLOps best practices, targeted problems, and use cases.

MLOps can be applied to different areas in machine learning to drive seamless integration of data science and IT operations teams to improve collaboration and automation to release more efficient and reliable machine learning projects.

Machine learning is a technology field with an enormous business potential that can only be realized using structured, business-oriented practices, without which machine learning may degrade into a mere experiment and liability for an organization, providing little or no business benefits.

MLOps is applied through the design, model development, and operations iterative phases to reach the full business potential of machine learning.

• MLOps in Design: The design phase is dedicated to the research and understanding of the machine learning project's business and data requirements. In this phase, the application's potential user is identified, and a suiting business model and machine learning solution to solve the user’s problem is designed. The software's possible use cases are also developed, and a check for the availability of necessary data to train the machine learning model is performed. Information gathered from each of these processes is then used to design the architecture of the ML-powered application.
• MLOps in Model Experimentation and Development: In the experimentation and development phase, the product from the previous design stage is then put to the test to validate the proposed ML solution's real-life implementation. A proof of concept (POC) is then implemented and run iteratively for the ML algorithm until a stable model that can run in production is achieved.
• MLOps in Operations: By using proven DevOps techniques, the stable model attained in the previous phase is then delivered into production. Testing, monitoring, versioning, automation, continuous delivery, and other DevOps processes are applied in this ML-powered application phase.

All of the phases above are interdependent; that is, one can only be used as a follow-up process of the previous one to achieve a user-ready machine learning project.

Principles of MLOps

Just like in DevOps, principles are guiding the operation and application of MLOps. However, these principles are similar to that of DevOps; MLOps is DevOps in machine learning.

While there may be more, below are six of the most important principles of MLOps:

1. Testing: Like in DevOps, a crucial part of software development and machine learning is not left out. MLOps provides a structured testing technique for machine learning systems based on the development pipeline's three essential components: data pipeline, ML model pipeline, and the application pipeline. The integration and usability of each feature are tested to ensure the reliability of the ML model.
2. Monitoring: Like testing, monitoring the ML model periodically when deployed is important and verified by this principle. To ensure that the model performs as expected, the dependency, data version, usage, and change made to the model are monitored. The model's desired behaviors should be pre-recorded and used as a benchmark which when the model underperforms or spikes irregularly, necessary actions are taken.
3. Versioning: Versioning ensures that every change made to the machine learning model's data sets and code base is tracked using a version control system. Considering that a significant number of events can bring about changes in data or cause an anomaly in a machine learning model, different versions of the model are tracked and saved by the version control tool. This makes it easy to revert to a previous version or know exactly where a bug is fixed when a problem arises.
4. Continuous “Everything”: One of the beauties of DevOps that is adopted in MLOps is the continuous workflow in the ML pipeline. Machine learning models are temporary and subject to change based on their use cases as soon as new data are available. MLOps allows for easy implementation of the ML engineering processes including continuous integration (CI), continuous delivery (CD), continuous testing (CT), and continuous monitoring (CM).
5. Automation: Automation should be prioritized to implement MLOps in your machine learning project successfully. The level of automation of your ML model determines your ML process's maturity, which in turn increases the speed at which machine learning models can be trained, developed, and deployed. This principle encourages an entirely automated ML workflow without a need for human interaction, with notable events used as triggers for each workflow process. Embracing automation with MLOps happens in three stages;

• Manual Process: This being the first stage, is the normal machine learning process where models are manually validated, tested, and executed iteratively to train the model for subsequent automated operations.
• ML Pipeline Automation: At this stage, continuous training is introduced to the model. When there are new data available, the validation and retraining process is automatically triggered without a manual intervention like in the first stage.
• CI/CD Pipeline Automation: Like in DevOps, continuous integration and delivery is introduced in the third to build automatically, test, and deploy machine learning models.

6. Reproducibility: Another important principle of MLOps to be taken care of is reproducibility. For MLOps to be successfully applied in machine learning, the design, data processing, model training, deployment, and other machine learning artifacts should be well stored to ensure that the model can be easily reproduced, provided the same data input.

Each of the principles above guides the effective application of MLOps, allowing teams to build production-ready machine learning products quickly.

Why Do We Need MLOps?

While all the phases and principles of MLOps sound interesting and are beneficial to machine learning workflows, a common question asked by developers and organizations is; we already know about AIOps, why do we need to implement MLOps?

A broad but straightforward answer to this question is that, despite their apparent similarities, MLOps is just implementing DevOps in machine learning projects and does not address the same problems as AIOps. To explain this further, let’s explore some of the benefits and reasons for MLOps.

Collaboration

When we look at the traditional setting of all teams working on an ML project, the software development, data science, and IT operations teams, we see an uncorrelated and isolated group of individuals with different expertise. MLOps bings a more collaborative process that makes all teams work hand-in-hand to combine their expertise in building more efficient, fast, and scalable machine learning models that leverage all fields.

Automation

The benefits of automation in software and machine learning development are very crucial to achieve desired business results. Rather than wasting time repeating the same processes involved in the ML-powered software's lifecycle, automation also helps various teams focus on more critical business issues to drive more fast and reliable business solutions.

Rapid Innovation

Improved collaboration and automation in machine learning by MLOps fosters rapid innovation and development of new feature-rich machine learning solutions.

Easy Deployment

MLOps makes it easy to deploy a machine learning algorithm through a streamlined process like that in DevOps. It improves the continuous integration, deployment, and delivery of machine learning models.

Effective Lifecycle Management

MLOps ultimately improves all production teams' efficiency and the entire machine learning project development workflow right from design to deployment.