What Is MLOps? - MATLAB
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      What Is MLOps?

      Machine learning operations, or MLOps, is a set of practices that guides organizations. MLOps provides guidelines on the full lifecycle of machine learning models. With these guidelines, MLOps automates the process of taking machine learning models to production and managing the models once they are in production.

      Heather Gorr explains what MLOps is and how to integrate it into development and operations, highlighting the key benefits of MLOps. MLOps has become relevant to many industries, as more organizations are incorporating machine learning into their operations.

      Published: 23 Jan 2024

      Machine learning operations-- or MLOps-- is a set of practices that guides organizations. It automates the process of taking machine learning models to production and managing the models once they're in production. MLOps provides guidelines on the full life cycle of machine learning models. It links the design, build, and test activities of typical machine learning with the deploy, maintain, and monitor activities of operations in a continuous feedback loop.

      More organizations are incorporating machine learning for data-driven and technology-driven applications. So naturally, MLOps has become relevant to many industries, such as automotive, energy production, and many more. Your organization might have invested a lot of resources developing machine learning models. But if they just sit unused on a desktop, they're not bringing value to the business. MLOps can change that.

      Whether your organization is already implementing MLOps, or you just want a better understanding of what and who is involved, by the end of this video, you will understand how to implement MLOps, its benefits, and the impact it will have on your organization. There are many moving parts and teams involved in taking machine learning models into production and maintaining them. On the machine learning side, the data scientists create the models. The machine learning engineers simulate and test the models as part of an AI system, which includes domain specific data management and analytics.

      When the models are deployed and enter the operations phase, IT professionals monitor and maintain the models. The teams involved in MLOps have different objectives and might work on separate platforms. This is why platform integration and collaboration between teams are so important in the efficient operation of machine learning models. And with collaboration and integration, the process of managing models in production becomes faster and more agile.

      Let's take a closer look at the steps involved in MLOps from data exploration and development of machine learning models to deployment and, of course, continuous monitoring and updates. Data can come from many sources. Data might include numeric data, images, or time series signals. Data types vary depending on the application, and a combination of data types might be used to train a machine learning model. Because of the variety and amount of data, managing this data is important, which means efficiently collecting, storing, and organizing the data.

      Real data tends to be contaminated by various noise sources. For example, sensor data commonly contains noise from the surrounding electronics and crosstalk from other sensors. Cleaning up the data and removing irrelevant information improves the data quality. This is typically a necessary step before feeding the data to a machine learning model. Feature extraction helps to reduce the amount of redundant data in increasingly complex data sets. By reducing the data dimensionality, the computational complexity of machine learning is reduced without sacrificing valuable information. In reality, you might need to apply additional methods to improve the quality of your data.

      Bias mitigation is a method that decreases bias and unfairness in machine learning models. That means it decreases the severity of stereotypical or unjust associations. There are dozens of classification and regression models, each taking a different approach to learning. These include support vector machines, linear and non-linear regression, naive Bayes classifiers, and deep learning networks.

      Explainable AI is an emerging field that aims to explain the behavior of models in intuitive ways, especially for deep learning. Understanding how models make predictions is key to establishing trust in AI decisions and deploying them to solve real problems. Choosing the right machine learning model can seem overwhelming. This choice requires evaluating trade-offs, such as model speed, accuracy, and complexity and can involve trial and error.

      AutoML automates time consuming repetitive tasks in machine learning development, such as feature extraction, model selection, and tuning. This automation lowers the level of expertise required to build accurate models. Success with machine learning requires more than training a model. It involves designing the system on which the model will run. Before deploying the model, the complete system should be simulated and tested.

      This is the right time for validating the AI system, before machine learning models enter production. Machine learning models gain value when deployed. To solve real problems, the models must leave the desktop and go into production. The machine learning model is integrated into an existing hardware or cloud-based system, for example, a patient monitoring app or a predictive maintenance system.

      Drift can be a big issue when machine learning models are used in real applications where data is often dynamic and always changing. A data drift can happen for many reasons, such as changes in the distribution of the input data over time or the relationship between the input and desired output. In MLOps, the performance of the models is constantly monitored by tracking metrics. Drift detection determines whether the model is operating within a predefined range. When drift is detected, the model needs retraining to adjust to the new dynamics, and then it can be redeployed into operation.

      By continuously evaluating and updating machine learning models, errors are reduced. Bringing MLOps to your organization or following and improving on your current MLOps infrastructure can seem daunting. But the benefits are too great to overlook. At a high level, MLOps brings automation of the machine learning lifecycle, continuous monitoring and updating of machine learning models, enabling collaboration between teams and platform integration, and gaining business value by efficiently operating machine learning models in production.

      Check out our video playlist to learn more about MLOps, how to apply machine learning and deep learning, and how to use AI with MATLAB and engineering applications.