MLOps stands for Machine Learning Operations and it is a set of practices with the purpose of developing and deploying machine learning models into operational apps faster and with more reliability. For non-technical people, you can think of it as the latest method to develop applications that interact with lots of dynamic information.

MLOps Model – A Closer Look

The specific practices involved in MLOps and their organization ensure that ML models can be deployed with more accuracy, can iterate efficiently as new information becomes available, and can be deployed with higher confidence. The individual practices include:

Data Collection

Machine learning models are typically deployed to solve challenges that involve large quantities of data, potentially from multiple sources. The first step is aggregating the data from multiple feeds into one platform. In the case of Wistron AiEdge Corp., since our solutions focus on the mobility industry, the data collected typically comes from videos, images, or sensors.

Data Processing

Understanding the data collected is the next critical step, it involves data cleansing, organizing the data and performing statistical methods to see the data distribution and evaluate whether it is analyzable. Videos and images are also tagged with the appropriate labels.

Development

Based on the results of data processing, the next step of program coding and model building can occur. The machine learning model is developed to interact with the results of the processed data.

Training

The next step is actually training the ML model to improve accuracy. This step involves processing data backwards and forward to the models, and programming to ensure precise results. In the case where visual capture is involved the goal is to make sure that recognition is highly precise.

Validation

The models next go through a validation procedure to ensure that the precision and recall rate are up to the required standards. As this is an iterative process that tracks the progress of each model version, the platform readily offers reports for each version.

Deployment

Following the validation process the AI applications are ready for implementation. Model updates continue to occur when users initiate deployments and previous versions are allowed to roll back whenever users need.

Functioning

The final stage is that of AI applications working in the field. This is the final step but it also kicks off the cycle again as data collection to help improve the next iteration starts simultaneously and the cycle starts again.

MLOps a Real-Life Example

So how might an MLOps deployment look like in a real-life situation? Let’s look at an example from our field of expertise – the railway industry.

A railway operation is a fast-paced organization that involves lots of moving parts like trains, passengers, and goods. To ensure that things run smoothly, safely, and efficiently, the operation has built-in many inspection checkpoints. These inspection checkpoints present just one opportunity for an MLOps developed AI application to help the operation work more efficiently and accurately.

One case for an AI application could be the utilization of visual data captured via video cameras to perform final inspection of trains before they leave a maintenance hub to return into operation. The MLOps can help develop the application that quickly learns how to identify all the critical items that need to be confirmed before a train can be given a green light to return to duty. Furthermore, the learning aspect of MLOps can help ensure that the model can accommodate new train models or additional inspection points as they get integrated into the system. The model can also learn how to react to changes such as the difference between day and night, as well as seasonal changes that may add additional factors such as snow into consideration.

Tangible Benefits of Implementing MLOps

In general, the utilization of MLOps helps speed up the development cycle, allows the platform to continuously learn and adapt to changes in the environment, and makes the development more transparent to the management team.

Specifically for a mobility business, if you identify operational inefficiency issues in your organization – we can help you investigate whether an MLOps-based solution can help you address those issues via development of AI-powered applications that can save you money, decrease energy usage, improve safety, and optimize the use of all your resources.

If you’re interested in learning more about our specific solution, visit our ZigFleet page for solutions focused on helping fleet management companies, or ZigRail for solutions that address the needs of railway operations.

To learn more about our mobility solutions, please contact us.