Principles of MLOps
Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models.
In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them.
What is MLOps?
MLOps is a broad field that encompasses all aspects of developing a machine learning model. In simple terms, it is a method of deploying and managing machine learning models. It maintains your entire machine-learning model (from the creative processes to the execution).
MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. MLOps acts as the link between data scientists and the production team’s operations (a team consisting of machine learning engineers, software engineers, and IT operations professionals) as they work together to develop ML models and supervise the use of ML models in production.
In terms of MLOps, each team involved in the processes continues to play an important role in their respective assignments. First, we have data scientists who are in charge of creating and training machine learning models. They might also help with data preparation and cleaning. The machine learning engineers are in charge of taking the models developed by data scientists and deploying them into production.
We also have software engineers who are in charge of creating and maintaining the facilities and tools required to deploy and operate machine learning models. They may also be involved in the deployment process’s automation. The IT operations professionals are then in charge of maintaining the hardware and software infrastructure required to run the machine learning models in production. They may also be involved in monitoring and troubleshooting.
As we can see, everyone has an important role to play.
Why MLOps?
One important thing to note about MLOps is that it streamlines a business’s approach to machine learning, and this helps teams become more flexible in the context of fast varying data and business specifications. This means that even in the business world, MLOps should be addressed.
MLOps merges the knowledge and skills of all ML operators (Data Scientists, ML experts, and so on) to enable more effective machine learning that utilizes all competencies. MLOps is not based on a specific ML field of study; rather, to achieve a product (the successful deployment of a machine learning model), various practitioners in the ML field must collaborate, resulting in a much more effective outcome.
MLOps enable organizations to improve the quality of their machine learning models, save time and resources, and establish a competitive lead. It can assist you in simplifying and automating the creation and operation of machine-learning models.
As shown in the above image, MLOps consists of a cycle, covering the entirety of the machine learning model training and production.
Comparing MLOps and DevOps
DevOps is a software development method that brings together multiple teams to organize and conspire to create more efficient and reliable products. Development and operations teams collaborate throughout the software application life cycle, from development and testing to implementation and operations.
One thing that DevOps and MLOps have in common is that they both emphasize process automation. Data is used to make accurate predictions; the software creates more effective processes, improves speed and service, and so on.
Real-time model analysis allows your team to track, monitor, and adjust models already in production. Learn more lessons from the field with Comet experts.
While there are similarities between MLOps and DevOps, they are also distinct in a number of ways. Continuous testing and training is a core part of MLOps, in addition to continuous integration and continuous delivery. However, in DevOps, the model offered by the software team does not change after testing and deploying, so continuous testing and training are not likely to be applied.
A machine learning system requires more testing and training than a software system. Model validation, data validation, and other MLOps tests are all applicable, but in a software system, aside from the basic testing, such as integration testing, not all of these tests are required.
Principles of MLOps
The following principles regulate machine learning operations:
Automation: The process of constructing pipelines from the beginning of model creation to training and deployment. The level of automation you use determines the maturity of your machine-learning processes. The speed at which new ML models are trained increases as maturity increases. Automated testing aids in the early detection of problems, allowing for faster error correction, and mistakes are learned from.
Continuous: As the name implies, this involves continuous integration, deployment, testing, and monitoring. This contributes to models being deployed quickly and consistently.
Testing: This relates to data testing, model development, and facilities. This should include procedures that ensure decision-making algorithms are aligned with business goals and relate to business impact metrics. As the models can affect the prediction quality, they should include up-to-date data that meets the business impact requirements.
Monitoring: Check to ensure that the ML models are performing as expected. Monitoring the performance and accuracy of deployed models and collecting user feedback to discuss future repetitions and improvements. When the real world presents new and unknown data or when the environment changes and the model’s learning sequence is altered, the model’s performance may decline over time. This is why monitoring is necessary to ensure that the model is functioning correctly.
Versioning: Versioning tracks and oversees changes to machine learning models and their related artifacts, such as code, data, and documentation. Versioning is an essential aspect of MLOps because it helps to ensure that machine learning models are reproducible, traceable, and maintainable. Versioning could be implemented through code, in which data scientists track and manage changes to their ML codes by tracking and managing the ML model itself or by tracking and managing the data used to train the model.
Reproducibility: Given the same inputs, each phase of data handling, model training, and implementation should generate identical results. The ability to recreate the results of a machine learning experiment or model is referred to as reproducibility. It is a crucial principle in MLOps because it contributes to the dependability and transparency of machine learning models.
How MLOps Works
It is important to note that one major characteristic in the field of machine learning is collaboration and reproducibility. A lack of collaboration and reproducibility is due to a variety of factors, including a lack of transparency, limited resources, and the fact that the field is rapidly advancing, making it difficult for researchers to keep up with the latest techniques and best practices.
MLOps solves this problem: It provides a framework and tools to help with the development, deployment, and upkeep of machine-learning models.
Because of the cyclical, non-siloed approach that MLOps takes, it’s easier for teams to collaborate across the machine learning lifecycle, including model production monitoring.
In general, this is how an operation takes place:
- Machine learning models are created by data scientists using tools and techniques such as data preparation, feature engineering, and model training.
- The trained machine learning models are deployed to a testing and validation environment.
- If the models pass the testing and validation stages, they are deployed to a production environment and used to make predictions or perform other tasks.
- In production, the performance of the machine learning models is monitored to ensure that they are performing as expected and to identify and resolve any problem that may occur.
- Data scientists may update and retrain machine learning models as new data becomes available or business needs change. The revised models are then implemented in production using the same technique.
Conclusion
We discussed the fundamentals of MLOps, its importance, how it works, and how it differs from DevOps. With these fundamentals in hand, one can now dive into putting this knowledge into practice.
Check out these resources to learn more about MLOps:
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